Category Archives: Somatic Stem Cells

What are the different kinds of stem cells? | American for …

There are three types of stem cells: adult stem cells, embryonic (or pluripotent) stem cells, and induced pluripotent stem cells (iPSCs).

Adult stem cells, or tissue stem cells, can come from different parts of the adult body. They are specific to a certain kind of tissue in the body: for instance, liver stem cells can regenerate liver tissue, and muscle stem cells can regenerate muscle fibers. But adult stem cells are limited to only becoming more of their specialized tissueliver stem cells cannot make new muscle fibers, nor can muscle stem cells make new liver tissue.

The thousands of different cell types that make up our bodies all came from one single master builder cell, called a pluripotent stem cell.

Pluripotent stem cells can be thought of as blank slates, because of their ability to build any cell type in the bodyskin cells, brain cells, muscle cells, etc. Unlike tissue stem cells, pluripotent stem cells are not limited to only becoming more of a certain tissue. Pluripotent stem cells primarily consist of embryonic stem cells, but the term now also encompasses another type of cells, called induced pluripotent stem cells. More on that later.

Induced pluripotent stem (iPS) cells are pluripotent cells that are derived from adult tissue using new scientific technology. They share characteristics with embryonic stem cells in that they can become any cell type in the body.

Reprogramming stem cells to create iPSCs involves some genetic manipulation, and this may cause some differences that are not present in cells that are already embryonic in nature. It is essential to continue research using all cell types. Because the field of stem cell research is so new, it is critical to explore all avenues of stem cell research, from pluripotent to tissue stem cells.

The process of generating an iPS cell line takes time and resources in a lab. To do so in a sterile and safe way in which the cells can be transplanted back into someone is even more expensive. It is also necessary that these cells undergo tests to ensure that they have not mutated or changed in any detrimental way through the reprogramming process. It is a cool idea that everyone could have their own iPS cell line that could be used to make a personalized therapy product for themselves, but in practice this is very time consuming and expensive to do it on a per-person basis. In embryonic stem cell therapies, the generation of the cells has already been performed in the proper ways, and the expensive tests can be performed on a single stem cell line, rather than a different line for every individual.

It is possible that one day iPSCs may prove to be equivalent to embryonic stem cells (ESCs) and could be used in the same way we use ESCs now. However, because iPSCs are a very new discovery (2006), it is still to be determined iPS cells are are equivalent to embryonic stem cells in all ways. Scientists are working hard on understanding the differences that may exist between embryonic stem cells and iPS cells, and we still have yet to determine which cell type will be the most useful for regenerative medicine.

What Are Stem Cells and Why Are They So Important? Stem cells are the builders

Research using pluripotent stem cells is legal in the United States. Federal courts, including the

Proposition 71 created the California stem cell program, formally titled the California Institute of Regenerative

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European Wellness Collaborates with Heidelberg University Germany to Conduct Efficacy Studies of Peptides and Cell Therapy Research – WILX-TV

Published: Oct. 27, 2021 at 4:30 AM EDT|Updated: 21 hours ago

FRANKFURT, Germany, Oct. 27, 2021 /PRNewswire/ European Wellness Academy (EWA), the educational arm of European Wellness Biomedical Group (EWG), has signed an agreement to carry out joint scientific research on the efficacy of peptides, cell therapy, exosomes and cell reprogramming for rejuvenation in premature murine aging models.

EWA was represented by its Group Chairman, Prof. Dr. Mike Chan, while Heidelberg University was represented by its Commercial Managing Director, Katrin Erk and its Head of Institute of Anatomy and Cell Biology III, Prof. Dr. Thomas Skutella.

The cutting-edge therapeutics used for the studies include precursor (progenitor) stem cells (PSC), precursor cells (Frozen Organo Crygenics (FOC)), Mito Organelle (MO), Nano Organo Peptides (NOP) and exosomes.

Their studies include in vitro experiments concentrating on the effects of the products on the aging of somatic cells and cellular senescence, which is known to contribute to disease onset and progression. Investigated exosomes include neuronal stem cells (NSCs), mesenchymal stem cells (MSCs), cardiomyocytes, kidney progenitors and hepatocytes.

EWA and Heidelberg University will also conduct in vivo experiments to demonstrate both safety and efficacy of the therapeutics, whereby the proof of effectivity will be recorded in the life span, histopathological and molecular criteria of neurodegeneration including Alzheimer/dementia, and system degeneration disorders including those affecting the immune system, skin, cardio, lung, kidney, liver, stomach/intestine/gut, eye, and muscular dystrophy.

Other criteria included are cartilage/joint/bone regeneration including knees/joints/hips, cervical, thoracic, lumbar, pelvic and musculoskeletal disorder, as well as endocrine disorders like endocrinal dysfunction due to over and underproduction of hormones and other activity pattern under the sleep wake cycle.

The ongoing specially designed studies are coordinated and designed by Prof. Dr. Thomas Skutella of Heidelberg University, a world-renowned research university and one of Germany's Top 3, Prof. Dr.Mike Chan and scientists of EWG.

European Wellness Academy

Located in Germany, Switzerland, Greece and Malaysia, EWA is a UK CPD authorised body with a premium training and development wing that revolves around cutting-edge Bio-Regenerative Medicine modalities for practitioners and researchers. The Academy has extensive years of combined clinical experience and a core academic team comprising of qualified clinicians and scientists with multiple international affiliations and accreditations.

https://ewacademy.eu https://european-wellness.eu/

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SOURCE European Wellness Biomedical Group

The above press release was provided courtesy of PRNewswire. The views, opinions and statements in the press release are not endorsed by Gray Media Group nor do they necessarily state or reflect those of Gray Media Group, Inc.

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The translatome of neuronal cell bodies, dendrites, and axons – pnas.org

Significance

Proteins are the key drivers of neuronal synaptic function. The regulation of gene expression is important for the formation and modification of synapses throughout the lifespan. The complexity of dendrites and axons imposes unique challenges for protein supply at remote locations. The discovery of messenger RNAs (mRNAs) and ribosomes near synapses has shown that local protein synthesis represents an important solution to this challenge. Here we used RNA sequencing and ribosome sequencing to determine directly the population of mRNAs that is present and in the process of translation in neuronal cell bodies, dendrites, and axons. Thousands of transcripts were differentially translated between the cell body and synaptic regions with over 800 mRNAs exhibiting more translation in the dendriticaxonal compartment.

To form synaptic connections and store information, neurons continuously remodel their proteomes. The impressive length of dendrites and axons imposes logistical challenges to maintain synaptic proteins at locations remote from the transcription source (the nucleus). The discovery of thousands of messenger RNAs (mRNAs) near synapses suggested that neurons overcome distance and gain autonomy by producing proteins locally. It is not generally known, however, if, how, and when localized mRNAs are translated into protein. To investigate the translational landscape in neuronal subregions, we performed simultaneous RNA sequencing (RNA-seq) and ribosome sequencing (Ribo-seq) from microdissected rodent brain slices to identify and quantify the transcriptome and translatome in cell bodies (somata) as well as dendrites and axons (neuropil). Thousands of transcripts were differentially translated between somatic and synaptic regions, with many scaffold and signaling molecules displaying increased translation levels in the neuropil. Most translational changes between compartments could be accounted for by differences in RNA abundance. Pervasive translational regulation was observed in both somata and neuropil influenced by specific mRNA features (e.g., untranslated region [UTR] length, RNA-binding protein [RBP] motifs, and upstream open reading frames [uORFs]). For over 800 mRNAs, the dominant source of translation was the neuropil. We constructed a searchable and interactive database for exploring mRNA transcripts and their translation levels in the somata and neuropil [MPI Brain Research, The mRNA translation landscape in the synaptic neuropil. https://public.brain.mpg.de/dashapps/localseq/. Accessed 5 October 2021]. Overall, our findings emphasize the substantial contribution of local translation to maintaining synaptic protein levels and indicate that on-site translational control is an important mechanism to control synaptic strength.

At neuronal synapses, more than 2,500 proteins (1, 2) (the synaptic proteome) act as sensors and effectors to control neuronal excitability, synaptic strength, and plasticity. The elaborate morphology and functional compartmentalization of the individual neuron imposes unique logistical challenges to maintain and modify the synaptic proteome at locations remote from the transcription source (i.e., the nucleus). To fulfill the local demand for new protein, neurons localize messenger RNAs (mRNAs) and ribosomes near synapses to produce proteins directly where they are needed (1). Using high-throughput sequencing, several groups have reported the localization of thousands of transcripts to axons and dendrites (the local transcriptome) (37). In many cell types, however, it has been shown that the transcript levels do not always predict protein levels (8), suggesting that mRNA translation is a highly regulated process. Since proteins, rather than mRNAs, drive cellular function, it is imperative to determine directly which transcripts are translated into proteins in dendrites and/or axons in vivo (the local translatome). Importantly, it remains unknown which transcripts exhibit differential levels of translation between somatic and synaptic regions.

A given transcripts translation level is determined by the rate of ribosome recruitment to the start codon during initiation and the velocity of ribosome translocation during polypeptide elongation. For most mRNAs, translation initiation is considered rate limiting (9): Initiation is regulated by elements within the mRNAs untranslated regions (UTRs) that bind RNA-binding proteins (RBPs) or miRNAs (1012). In addition, the elongation rate also plays a regulatory role in determining the amount of protein produced from a transcript (13). Although disrupted translational control has been linked to a number of neurological disorders (14), little is known about the magnitude and mechanisms for transcript-specific translational regulation in neuronal compartments.

In this study, we combined deep sequencing of ribosome-protected fragments (ribosome sequencing [Ribo-seq]) and RNA sequencing (RNA-seq) of microdissected hippocampal rodent brain sections to provide a comprehensive analysis of the mRNA translational landscape both in the somata (enriched in cell bodies) and the neuropil (enriched in neuronal dendrites/axons). Thousands of mRNAs were translated in the somatic and synaptic regions. Many transcripts exhibited differential translation levels between somatic and synaptic regions. Many of these translational changes likely resulted from differences in the RNA levels between the somata and neuropil. Furthermore, we found evidence for pervasive translational regulation of synaptic proteins in both neuronal compartments. We provide a dynamic query-based web interface for exploring mRNA transcripts and their translation in neuronal compartments (15). Together, our results reveal an unprecedented capacity for local protein production in vivo to maintain and modify the pre- and postsynaptic proteome.

To discover the mRNA species localized and translated in cell bodies as well as dendrites and axons we carried out a genome-wide analysis of the transcriptome and translatome of the somata and neuropil from microdissected hippocampal slices (16). Ribosome footprints were obtained from somata and neuropil lysates to assess the number and position of translating ribosomes on a transcript (Ribo-seq) (17). In parallel, transcript levels were quantified by performing RNA-seq from the somata and neuropil (Fig. 1A) (16). The RNA- and Ribo-seq libraries from the somata and neuropil were highly reproducible among the three biological replicates (SI Appendix, Fig. S1 A and B). Furthermore, the Ribo-seq samples exhibited the expected depletion of footprint read densities in the UTRs and introns of transcripts (SI Appendix, Fig. S1 C and D), as well as three-nucleotide phasing (SI Appendix, Fig. S1 E and F) (17).

Many transcripts display differential translation between the somata and neuropil. (A) Experimental workflow. Microdissection of the CA1 region of the rat hippocampus. RNA-seq and Ribo-seq were conducted simultaneously for the somata (enriched in pyramidal neuron cell bodies) and the neuropil (enriched in dendrites and axons) layers. A neuronal filter was applied to enrich for excitatory neuron transcripts in downstream analyses. (B) Volcano plot comparing the translational level of 7,850 transcripts between compartments (neuropil:somata Ribo-seq ratio [log2FC]). FDR < 0.05 using DESeq2 (Experimental Procedures). Colored dots highlight the transcripts significantly more translated in the somata (somata [smt]-translation-up, n = 2,945, orange) or neuropil (neuropil [npl]-translation-up, n = 807, teal). (C) Coverage tracks representing the average neuropil (Top) or somata (Bottom) ribosome footprint coverage for candidate smt-translation-up (Gria2, Neurod6, and Hpca) and npl-translation-up (Shank1, Map2, and Dgkz) transcripts. The y axis indicates the number of normalized reads. (D) Schematic depicting in vivo ribosome run-off following harringtonine incubation of rat hippocampal cultures. (E) Elongation rates for smt-translation-up (orange), npl-translation-up (teal), and other (gray) transcripts inferred from the slope of the linear fit shown in SI Appendix, Fig. S4 are plotted with their SE (n = 3). P = 0.5738, One-way ANOVA. Har, harringtonine; Chx, cycloheximide; ns, not significant.

We detected 13,055 and 12,371 transcripts with one count per million (CPM) in two of three neuropil (SI Appendix, Fig. S2A) or somata (SI Appendix, Fig. S2B) Ribo-seq replicates, respectively. Using the Ribo-seq datasets, we found substantial overlap between our translatome data and a previously published neuropil (SI Appendix, Fig. S2A) and somata (SI Appendix, Fig. S2B) transcriptome (3). The somata and neuropil of the hippocampus contain excitatory neuron cell bodies and their processes, as well as glia and interneurons. We created a pipeline to focus on excitatory neuron genes by minimizing the contribution of other cell types via bioinformatic filtering. To obtain a comprehensive set of glia-enriched transcripts, we prepared hippocampal neuron- and glia-enriched cultures (SI Appendix, Fig. S2C and Dataset S1). Because the somata and neuropil do not only contain glia but also interneurons, we additionally compiled lists of transcripts enriched in nonexcitatory neuron cell types in the hippocampus. To do so, we identified the transcripts significantly deenriched in the hippocampi of two different RiboTag mouse lines that target primarily excitatory neurons: Camk2Cre::RiboTag mice (SI Appendix, Fig. S2D), as well as the microdissected somata (SI Appendix, Fig. S2E) and neuropil (SI Appendix, Fig. S2F) from Wfs1Cre::RiboTag mice (16). Combining these datasets, we obtained a list of contaminant nonexcitatory neuron genes (SI Appendix, Fig. S2G).

The number of ribosomes loaded on a transcript indicates how much it is translated. To identify transcripts that exhibit differential translation between the somata and neuropil, we computed neuropil:somata Ribo-seq ratios (DESeq2) (18) (Experimental Procedures). After subtraction of the contaminant genes, we detected 7,850 neuronal transcripts (SI Appendix, Fig. S2H) (19) that were translated in both the somata and neuropil (Fig. 1B). Of these, 807 transcripts exhibited significantly increased translation levels in the neuropil compared to the somata (neuropil-translation-up) (Fig. 1B and Dataset S2). The neuropil-translation-up transcripts included, for example, Shank1, Map2, and Dgkz (Fig. 1 B and C). In contrast, 2,945 transcripts showed increased translation in the somata, including Gria2, Neurod6, and Hpca (somata-translation-up) (Fig. 1 B and C and Dataset S2). Both neuropil- and somata-translation-up transcripts exhibited three-nucleotide periodicity arising from the codon-by-codon translocation of ribosomes along mRNAs during translation in the neuropil and somata, respectively (SI Appendix, Fig. S3 A and B). Consistent with previous findings (12), the neuropil-translation-up transcripts displayed significantly longer 3 UTRs (SI Appendix, Fig. S3C).

Previous studies suggested that mRNAs present in dendrites and/or axons might be translationally silenced, via the pausing of ribosomes at the level of elongation (13, 20). To address this, we asked whether the neuropil- and somata-translation-up transcripts exhibited differences in the speed of translation elongation. We performed a time series of ribosome run-off by incubating cultured hippocampal neurons for 15, 30, 45, or 90 s with harringtonine, a drug that immobilizes ribosomes immediately after translation initiation, resulting in a progressive run-off of ribosomes over time (Fig. 1D and SI Appendix, Fig. S4). We analyzed the rate of ribosome progression (elongation) from the 5 end of neuropil- and somata-translation-up transcripts (SI Appendix, Fig. S4). The neuropil- and somata-translation-up transcript subsets displayed a similar elongation rate of 4 codons per second (Fig. 1E and SI Appendix, Fig. S4), a value that is within the range measured in other cell types (3 to 10 codons per second) (2124). Together, these findings indicate that neuropil-translation-up mRNAs are globally not significantly more paused than other transcripts.

To examine whether particular protein function groups are encoded by transcripts that exhibit increased translation levels in either compartment, we performed a gene ontology (GO) analysis (Fig. 2 A and B). An enrichment of terms associated with synaptic function was found for both somata- and neuropil-translation-up transcripts (Fig. 2 A and B). For the somata-translation-up transcripts, we observed a significant overrepresentation of the term perikaryon as well as many membrane-related terms such as integral component of postsynaptic density membrane, presynaptic membrane, or synaptic vesicle membrane (Fig. 2A). On the other hand, mostly postsynaptic functions were significantly associated with the neuropil-translation-up transcripts, including for example dendritic spine and postsynaptic density (Fig. 2B). To understand better the synaptic function of the neuropil- and somata-translation-up transcripts, we analyzed the neuropil:somata Ribo-seq fold changes of excitatory synaptic proteins (Fig. 2C). We noted that ionotropic and metabotropic glutamate receptor subunits (AMPARs, NMDARs, and mGluRs) mostly displayed greater translation levels in the somata (Fig. 2C). In contrast, many glutamate receptor-associated accessory (e.g., Cnih2) or scaffold proteins (e.g., Shank1, Dlg4, and Homer2) exhibited increased translation levels in the neuropil (Fig. 2C). Also, we found that many presynaptic proteins exhibited greater protein synthesis rates in the somata (Fig. 2C). Interestingly, we identified several nuclear-encoded mRNAs related to mitochondrial function that exhibited enhanced translation levels in the neuropil (e.g., Timm8a1 and Mrpl40) (Fig. 2C).

Functional segregation of transcripts differentially translated between the somata and neuropil. (A and B) GO terms representing the top five highest significantly enriched (FDR < 0.05) protein function groups for somata-translation-up (A) and neuropil-translation-up (B) transcripts. (C) Scheme depicting proteins of glutamatergic synapses. Ribo-seq neuropil:somata ratios (log2FC) are color coded from orange (more somata-translated) to teal (more neuropil-translated). Interacting proteins are displayed in closer proximity. Proteins with similar functions are grouped together and the synaptic vesicle cycle is indicated by arrows.

The mRNA transcript and translation profiles in the somata and neuropil are available for download and exploration at a searchable web interface (https://public.brain.mpg.de/dashapps/localseq/). This interactive database allows viewers to compare transcript and mRNA translation levels between neuronal compartments.

The translation level of a given transcript is proportional to its abundance and its ribosome density. We thus asked whether differential translation of somata- and neuropil-translation-up transcripts was associated with between-compartment changes in RNA levels (Dataset S3). Indeed, neuropil-translation-up transcripts displayed significantly higher neuropil:somata RNA-seq ratios compared to somata-translation-up genes (Fig. 3A). In order to validate these observations in situ in hippocampal slices, we performed high-resolution fluorescence in situ hybridization (FISH) for 14 candidate transcripts with significantly different translation levels between the somata and neuropil (Fig. 3 BD). The in situ hybridization signal detected was highest in expected compartment (i.e., somata for somata-translation-up, Fig. 3 B and D, and neuropil for neuropil-translation-up, Fig. 3 C and D). Taken together, both the RNA-seq and FISH analyses revealed that increased translation in the somata or neuropil was accompanied by higher RNA levels in the same neuronal compartment.

Differential translation of neuropil- and somata-translation-up genes is accompanied by between-compartment changes in RNA levels. (A) Box plot representing the neuropil:somata RNA-seq ratio (log2FC) for somata (smt)-translation-up (orange) and neuropil (npl)-translation-up (teal) genes (DESeq2; Experimental Procedures). (B and C) (Top) Neuropil:somata RNA-and Ribo-seq ratios (log2FC) for candidate smt-translation-up genes (Gria2, Cacng8, Uchl1, Sv2b, Syp1, Gria1, and Snap25) (B) and npl-translation-up genes (Aco2, Dlg4, Hpcal4, Cnih2, Ddn, Eef2, and Camk2a) (C). (Bottom) FISH signal in the CA1 region of rat hippocampal slices using probes against smt- (B) and npl-translation-up (C) candidate genes. The dendrites were immunostained with an anti-MAP2 antibody (purple). (Scale bar, 50 m.) (D) Neuropil:somata ratio of mRNA puncta relative to the mean neuropil:somata ratio of the smt-translation-up genes (***P < 2.2e-16, MannWhitney U Test between all smt-translation-up and all npl-translation-up genes).

We next compared gene-level translation efficiencies (TEs) between the neuropil and somata by computing the ratio of ribosome footprints (from Ribo-seq) to mRNA fragments (from RNA-seq) (17) in both compartments (Fig. 4A and Dataset S4). We observed a good correlation between the somata and neuropil TE values, indicating that most transcripts exhibit similar translational regulation in both neuronal compartments (Fig. 4A, R2 = 0.92, P < 2.2e-16). For instance, Syngap1 exhibited low footprint-to-mRNA ratios in both somata and neuropil, indicating the relatively poor translational efficiency of this transcript (Fig. 4 A and B). In contrast, Camk2a was found translated with high efficiency (high footprint-to-mRNA ratio) in both neuronal compartments (Fig. 4 A and B). We also identified a handful of mRNAs that displayed significantly higher TE values in the somata, including, for example, Kif5c (Fig. 4 A and B). Thus, many but not all of the between-compartment differences in ribosome footprint levels can be accounted for by differences in the amount of mRNA present.

Most transcripts exhibit similar translational efficiency in the somata and neuropil. (A) Correlation of the translational efficiencies (TE; log2Ribo-Seq/RNA-seq) in the neuropil and somata (R2 = 0.92, P < 2.2e-16). Highlighted are genes with significantly higher (TEhigh, yellow) or lower (TElow, blue) TE than log2 1.5 (FDR < 0.05, DESeq2) in both somata and neuropil. Genes with significantly differential TE between somata and neuropil are shown in red. DESeq2 with FDR <0.05. Marginal rug (gray) represents the distribution of the TE values in the somata (x axis) and neuropil (y axis). (B) Coverage tracks representing the average ribosome footprint or RNA coverage for candidate genes (Syngap1, Kif5c, and Camk2a) in the neuropil and somata. The y axis indicates reads per million (RPM). (C and D) GO terms representing significantly enriched (FDR < 0.05) protein function groups for TElow (C) and TEhigh (D) transcripts. (E) Empirical cumulative distribution frequency (Ecdf) of the TE (log2FC) of SFARI autism associated (yellow) and other (black) genes. P = 2.579e-05, KolmogorovSmirnov test.

In both neuronal compartments, we observed a wide distribution of translation efficiencies, with a greater than 1,000-fold difference between the most and least efficiently translated transcripts in the neuropil (Fig. 4A). We identified 730 and 592 transcripts exhibiting significantly high or low translational efficiencies, respectively, in both somata and neuropil (Fig. 4A and Dataset S4). We identified gene features associated with these two groups which we call TElow and TEhigh. GO analysis revealed an enrichment of terms such as spindle and microtubule organizing center for TElow genes (Fig. 4C). In contrast, TEhigh genes were associated with terms such as intrinsic component of synaptic vesicle membrane and intrinsic component of postsynaptic membrane (Fig. 4D). As a group, TElow transcripts had longer coding sequences (CDS), consistent with previous observations (2527) (SI Appendix, Fig. S5A). Because autism risk factor genes have been described to be exceptionally long (2830), we analyzed the TE values of Simons Foundation Autism Research Initiative (SFARI) transcripts. We found that SFARI transcripts displayed overall lower TE values compared to other genes (Fig. 4E). The efficiency of mRNA translation is also influenced by elements within the UTRs that serve as binding platforms for regulatory RBPs (10, 12). Because longer UTRs harbor more cis-acting elements (10, 12), we examined the 5 and 3 UTR length of the translationally regulated transcripts. We found that TElow genes exhibited significantly longer 5 and 3 UTRs (Fig. 5 A and B). To identify potential RBPs for the neuropil UTRs, we searched for known RBP consensus motifs (31) and determined whether transcript groups sharing the same motifs were associated with higher or lower TE values in the neuropil (Experimental Procedures). A total of 131 3 UTR motifs targeted by 52 RBPs (Dataset S5) were associated with transcripts displaying significantly higher TE values in the neuropil (Fig. 5C; for somata see SI Appendix, Fig. S5B and Dataset S6). For example, consistent with their described role as translational enhancers (3234), HNRNPK and MBNL1 motifs were detected in transcripts exhibiting significantly higher TE values (Fig. 5C). On the other hand, 155 3 UTR motifs targeted by 90 RBPs (Dataset S5) were associated with transcripts exhibiting significantly lower neuropil TE values in the neuropil (Fig. 5C). Among these, we identified, for example, the CPEB, Hu (Elav), and PUF/Pumilio RBP families, all known for their repressive action on translation in neuronal processes (35). We note that none of the RBP motifs we detected within neuropil 5 UTRs were associated with transcripts displaying significantly higher or lower neuropil or somata TE (Datasets S7 and S8). Our results thus reveal the identity of potentially novel regulators that bind the 3 UTR and control translation, either directly or indirectly for example via the regulation of polyadenylation (34) or mRNA decay (35).

Features of translationally regulated transcripts in the somata and neuropil. (A and B) Box plots of 5 UTR (A) and 3 UTR (B) length (log10 nucleotides (nts) for TEhigh (yellow), TElow (blue), and other (gray) genes. Bars indicate 1.5*IQR. *P < 0.05, ****P < 0.0001; one-way ANOVA test followed by pairwise t test with BenjaminiHochberg P value adjustment. (C) Shown are RBP motifs within 3 UTRs associated with significantly lower (blue) or higher (yellow) neuropil TE values (q values < 0.05; Wilcoxon rank sum test) (Experimental Procedures). (D) Detection of translated uORFs in hippocampal neurons. Translation initiation sites were mapped using the drug harringtonine (har), which accumulates ribosomes at start codons. A total of 766 uORF-containing neuronal transcripts were detected in the somata and neuropil. (E) Coverage tracks representing the average ribosome footprint reads along the UTRs (gray), detected uORFs (orange), or the main protein coding sequence (blue) of Dlg4, Gria2, Taok1, and Ppp1r9b in the neuropil. The y axis indicates reads per million (RPM). (F) Observed-to-expected ratio of TEhigh (teal), TElow (blue), and other (gray) transcripts containing uORFs. **P < 0.01, ***P < 0.001, ****P < 0.0001; hypergeometric test. (G) Neuropil TE (log2FC) measurements of transcripts containing translated uORFs (uORF) or not (no uORF). ****P < 0.0001; Welch two-sample t test. (H) GO terms representing the top eight significantly (FDR < 0.05) enriched protein function groups for uORF-containing transcripts in the neuropil.

Upstream open reading frames (uORFs) also play an important role in regulating the translation of the main protein coding sequence (36). While most uORFs are believed to exert a negative effect on the translation of downstream ORFs (36), a few examples of positive-acting uORFs have been reported (37, 38). We identified translated uORFs in neuronal compartments using an integrated experimental and computational approach. To map upstream translation initiation sites within neuronal transcripts, we performed Ribo-seq on neurons treated with the drug harringtonine, which causes the accumulation of ribosomes at start codons (21) (Fig. 5D and Experimental Procedures). We then used the ORF-RATER pipeline to identify and quantify translated uORFs in the neuropil- and somata Ribo-seq data (Experimental Procedures) (39). In total, we identified 766 uORF-containing mRNAs in neuronal compartments (Fig. 5D and Dataset S9), including novel (e.g., Gria2, Taok1, Dlg4, and Ppp1r9b) (Fig. 5E and SI Appendix, Fig. S5C) and previously described (e.g., Atf4 and Ppp1r15b) (38, 40) (SI Appendix, Fig. S5D) transcripts. A comparison of TElow and TEhigh transcripts revealed an overrepresentation of uORF-containing transcripts in the TElow group and an underrepresentation of uORF-containing transcripts in the TEhigh group (Fig. 5F). Additionally, uORF-containing transcripts displayed a significantly lower neuropil median TE value when compared with non-uORF-containing mRNAs (Fig. 5G and SI Appendix, Fig. S5E for the somata). Using the neuropil Ribo-seq data, we next computed a relative uORF to CDS ribosome density for each uORF. Of interest, the relative uORF:CDS ribosome densities ranged from 0.1 to 1,000, indicating a wide spread in the uORF-mediated translational repression in the neuropil (SI Appendix, Fig. S5F). Many uORFs displayed uORF:CDS ribosome density ratios greater than 1, indicating that uORFs often act as CDS translational repressors. A GO analysis indicated that above described uORF-containing neuropil and somata mRNAs were significantly enriched for terms like positive regulation of synapse assembly, regulation of membrane potential, and behavior (Fig. 5H). These findings highlight uORFs as an important translational regulatory element present in many transcripts in somatic and synaptic regions.

Using ribosome profiling, we detected thousands of mRNA species that are translated in synaptic regions, dramatically expanding the contribution of ongoing local protein synthesis to the protein pool detected in dendrites, axons, or synapses (4144). Indeed, among the locally translated mRNAs, we identified most protein families, including signaling molecules (kinases or phosphatases), ion channels, metabotropic and ionotropic receptors, cell adhesion molecules, scaffold proteins, as well as regulators of cytoskeleton remodeling or translation.

Many transcripts were found differentially translated between neuronal compartments. An open question in the field has concerned the contribution of local synthesis to the total pool of a particular protein. Our data indicate that most proteins are synthesized in both compartments. We note that over 800 mRNAs displayed enhanced translation levels in the neuropil, suggesting that most of these proteins arise from a local source. For many transcripts, the abundance of the mRNA was positively associated with the translation level differences between somata and neuropil, as observed previously in developing neurons derived from mouse embryonic stem cells (45). Notably, the neuropil-translation-up transcripts often encoded signaling and scaffold proteins that play an important role in the maintenance and modification of synaptic strength. Of interest, we detected several mitochondrial mRNAs that displayed enhanced neuropil translation. Recently, it has been shown that endosomes can act as platforms for the local translation of candidate mitochondrial mRNAs (46). It is thus tempting to hypothesize that local translation plays a role in sustaining mitochondria, which in turn fuel protein synthesis near synapses during plasticity (47). Together, our results suggest that the increased translation levels of a specific transcript subset in the neuropil likely provide a means to ensure the efficient production of key synaptic proteins at very remote locations from the cell body.

In contrast the transcripts with increased translation levels in the somata often encoded transmembrane proteins. This protein class is typically processed through multiple membrane-bound organelles (including the endoplasmic reticulum [ER] and Golgi apparatus [GA]), where they are folded, assembled, and biochemically modified prior to their delivery to the neuronal cell surface (48). However, recent studies reported that hundreds of neuronal surface proteins (e.g., the AMPAR subunit GluA1) bypass GA maturation and likely travel directly from the ER to the neuronal cell surface (49, 50). Thus, although the bulk synthesis and posttranslational modification of transmembrane proteins might occur in the somatic ER and GA, a small residual fraction of this protein class could undergo on demand local translation to fine tune synaptic strength.

Using a combination of microdissection with Ribo-and RNA-seq, we found that most transcripts exhibit similar translational regulation in the somata and neuropil. In both neuronal compartments, we detected widespread translational regulation, with an unexpectedly high dynamic range in the translation efficiencies of transcripts. Among the mechanisms that regulate the synthesis of proteins in somatic and synaptic regions, we identified uORF-mediated translational control. This finding is in good agreement with previous studies revealing the role of uORFs in the translational regulation of two candidate transcripts in neuronal processes (51, 52). uORF-mediated translational control is often fine tuned by the phosphorylation of eukaryotic initiation factor 2 (eIF2) (53). The phosphorylation of eIF2 inhibits global translation while leading to a paradoxical increase in the translation of a subset of uORF-bearing transcripts (54). Many manipulations of cellular and synaptic activity modulate the phosphorylation status of eIF2 in neurons in vivo and in vitro (5457). Thus, activity-driven eIF2 phosphorylation could act as a switch to enhance the local translational efficiency of uORF-containing transcripts encoding key plasticity-related proteins. It is noteworthy that the translational regulation of some uORF-containing transcripts is insensitive to changes in the eIF2 phosphorylation status (e.g., the protein phosphatase 1 regulatory subunit CReP [Ppp1r15b]) (40).

Electron microscopy (EM) studies have shown that the distribution of the ribosomes along neuronal processes is heterogeneous, with a selective localization of protein-making machines (i.e., polyribosomes, more than three ribosomes per mRNA) beneath synapses, while only a few polyribosomes could be observed in CA1 dendritic shafts (58, 59). Dendritic shafts could be mostly populated by monosomes (i.e., single ribosome per mRNA) that cannot be visualized by EM but also represent active protein making machines in synaptic regions (16). Indeed, a recent superresolution study which likely detects both monosomes and polysomes identified a greater ribosome density in dendrites compared to EM studies (60). These observations raise intriguing questions about the definition of local translation compartments: Are different protein species synthesized within distinct subregions of neuronal processes (e.g., spines vs. dendritic shafts)? And: Could the translation efficiency of the same transcript vary depending on whether it is localized beneath synapses or in other dendritic regions? These questions set the stage for future studies characterizing the translational landscape in neuronal subregions with greater spatial resolution using, for example, proximity-specific ribosome profiling.

Timed pregnant specific-pathogen-free (Charles River Laboratories) female rats were housed in Max Planck Institute for Brain Research animal facility for 1 wk on a 12/12-h light/dark cycle with food and water ad libitum until the litter was born. Cultured neurons were derived from P0 (postnatal day 0) Sprague-Dawley rat pups (both male and female, research resource identifier: 734476). Pups were killed by decapitation. The housing and killing procedures involving animal treatment and care were conducted in conformity with the institutional guidelines that are in compliance with national and international laws and policies (Directive 2010/63/EU; German animal welfare law; Federation of European Laboratory Animal Science Associations guidelines). The animals were killed according to annex 2 of 2 Abs. 2 Tierschutz-Versuchstier-Verordnung. Animal numbers were reported to the local authority (Regierungsprsidium Darmstadt, approval numbers: V54-19c20/15-F126/1020 and V54-19c20/15-F126/1023).

Total Ribo-seq (including monosomes and polysomes) and RNA-seq libraries from microdissected rat somata and neuropil of three biological replicates were generated previously (16) (SI Appendix, Table S1). In short, somata and neuropil were microdissected from 4-wk-old male rats. The tissue samples were homogenized in polysome lysis buffer (20 mM Tris pH 7.5, 150 mM NaCl, 5 mM MgCl2, 24 U/mL TurboDNase, 100 g/mL cycloheximide, 1 mM dithiothreitol (DTT), 1% Triton X-100, and protease inhibitor mixture [Roche]) by douncing in a glass homogenizer. After triturating the lysate 10 times using a 23-gauge syringe, samples were chilled on ice for 10 min and cleared by two centrifugations at 16,100 g for 6 min. From the somata and neuropil lysates Ribo-seq and RNA-seq libraries were prepared simultaneously. For Ribo-seq, neuropil and somata lysates containing equal amounts of total RNA were digested with 0.5 U/g RNase I (Epicentre), shaking for 45 min at 400 rpm at 24C. Nuclease digestion reactions were promptly cooled and spun, and 10 L of SUPERaseIN*RNase inhibitor was added. Samples were then layered onto a 34% sucrose cushion, prepared wt/vol in gradient buffer supplemented with 20 U/L of SUPERaseIN*RNase inhibitor. 80S particles were pelleted by centrifugation in a SW55Ti rotor for 3 h 30 min at 55,000 rpm at 4C. Ribo-seq libraries were prepared according to ref. 61 with the modifications described in ref. 16. Total RNA was isolated from tissue lysates using the Direct-zol RNA micro prep kit (Zymo). RNA integrity was assessed using the Agilent RNA 6000 Nano kit. Rat neuropil and somata total RNA-seq libraries were prepared from an equal amount of total RNA using the TruSeq stranded total RNA library prep gold kit (Illumina) (16). Libraries were sequenced on an Illumina NextSeq500, using a single-end 52- and 75-bp run for Ribo-seq and RNA-seq, respectively.

Neuron-enriched and glia-enriched cultures were prepared from the same litter as described previously (12). The hippocampi of P0-d-old rat pups were isolated and triturated after digestion with papain. Both cultures were plated on 60-mm cell culture dishes. For the preparation of hippocampal neuron-enriched cultures, cells were plated onto poly-d-lysine-coated 60-mm cell culture dishes and treated as described above with Ara-C (Sigma) at a final concentration of 5 M for 48 h. After 48 h, the medium was replaced with preconditioned growth medium and cells were cultured until 21 d in vitro (DIV). For the preparation of glia-enriched cultures, cells were plated onto uncoated 60-mm cell culture dishes in conditioned minimal essential medium (minimal essential medium, 10% horse serum, 0.6% glucose [wt/vol]). At 7 DIV, the medium was replaced with preconditioned growth medium and cells were cultured until 21 DIV. Four independent biological replicates were prepared. RNA was isolated using the Direct-zol RNA micro prep kit (Zymo). RNA integrity was assessed using the Agilent RNA 6000 Nano kit. mRNA-seq libraries were prepared starting from 200 ng of total RNA, using the TruSeq stranded mRNA library prep kit (Illumina). Libraries were sequenced on an Illumina NextSeq500, using a single-end, 75-bp run.

The input- and translating ribosome affinity purification (TRAP)-seq libraries from hippocampi of Camk2a-Cre-RiboTag or somata/neuropil sections of Wfs1-Cre-RiboTag mice were generated previously (16) (SI Appendix, Table S1).

Dissociated rat hippocampal neurons were prepared from P0-d-old rat pups as described previously (62). Hippocampal neurons were plated at a density of 31,250 cells/cm2 onto poly-d-lysine-coated 100-mm dishes and cultured in preconditioned growth medium (Neurobasal-A, B27, GlutaMAX, 30% glia-culture supernatant, 15% cortex-culture supernatant) for 21 DIV. At 1 DIV, cells were treated with Ara-C (Sigma) at a final concentration of 5 M to prevent the overgrowth of nonneuronal cells. After 48 h, the medium was replaced with preconditioned growth medium and cells were cultured until 21 DIV. Cells were fed with 1 mL of preconditioned medium every 7 d. Three independent biological replicates were prepared. At 24 h before drug treatment, cell medium was adjusted to 8 mL per dish. In appropriate experiments, harringtonine (LKT Laboratories) was added to a final concentration of 2 g/mL from a 5 mg/mL stock in 100% ethanol. Cells were returned to the incubator at 37C for 15, 30, 45, 90, or 150 s. Cycloheximide was added to a final concentration of 100 g/mL from a stock of 50 mg/mL in 100% ethanol. After drug addition, cells were returned to the incubator at 37C for 1 min. After the incubation with cycloheximide, the cells were immediately placed on ice and washed twice with ice-cold phosphate-buffered saline (PBS) plus 100 g/mL cycloheximide and scraped in polysome lysis buffer (20 mM Tris pH 7.5, 150 mM NaCl, 5 mM MgCl2, 24 U/mL TurboDNase, 100 g/mL cycloheximide, 1 mM DTT, 1% Triton-X-100, and protease inhibitor mixture [Roche]) (21). After scraping, the lysates were triturated 10 times using a 23-gauge syringe; samples were chilled on ice for 10 min and then cleared by centrifugation at 16,100 g for 10 min. Ribo-seq libraries from rat hippocampal neuron cultures treated for 0, 15, 30, 45, 90, and 150 s with harringtonine were prepared as described above. The 0-, 30-, and 90-s datasets were previously published in ref. 16 (SI Appendix, Table S1).

Four-week-old male rats were perfused with 1 RNase-free PBS and fixative solution (4% (vol/vol) paraformaldehyde (PFA), 4% (wt/vol) sucrose in 1 RNase-free PBS). Brains were dissected and fixed for another hour at room temperature. Brains were cryoprotected for two consecutive days at 4C. In 15% (wt/vol) sucrose in RNase-free 1 PBS on day 1, followed by 30% (wt/vol) sucrose in RNase-free 1 PBS on day 2. Hippocampi were cryosectioned at 30-m thickness.

Fluorescence in situ hybridization was performed using the QuantiGene ViewRNA kit (Thermo Fisher) mostly following the manufacturers instructions. In brief, hippocampal slices were postfixed for 10 min at room temperature in fixative solution (4% [vol/vol] PFA, 5.4% [wt/vol] glucose, 0.01 M sodium metaperiodate in 1 lysine-phosphate buffer). The manufacturer recommended proteinase K treatment was omitted to preserve the integrity of the dendrites. Slices were permeabilized for 20 min using the kits detergent buffer. Detection probes were incubated overnight at 40C. Preamplification, amplification, and label probes were incubated for 60 min at 40C, respectively, washing three times for 5 min between each step. After completion of in situ hybridization, slices were washed with 1 PBS and incubated in blocking buffer (4% [vol/vol] goat serum 1 PBS) for 1 h at room temperature. The primary antibody (gp-anti-MAP2, SYSY 188004, 1:1,000) was incubated overnight in blocking buffer at 4C. Slices were washed five times for 10 min in 1 PBS and the secondary antibody (gt-anti-gp Alexa 647, Thermo Fisher A21450, 1:500) was incubated in blocking buffer for 5 h at room temperature. Slices were washed in 1 PBS and nuclei were stained with DAPI for 3 min at room temperature. Slices were mounted in AquaPolyMount.

Slices were imaged using a Zeiss LSM780 confocal microscope and a 40 oil objective (numerical aperture [NA] 1.3). Z stacks spanning the entire slice volume were obtained using appropriate excitation laser lines and spectral detection windows. The mRNA signal was dilated for better visualization. The raw, nondilated images were used for analysis.

An in-house Python script was used to count mRNA puncta in the somata and the neuropil layer, respectively. In the neuropil, puncta colocalizing with DAPI signal (arising from glia or interneurons) were excluded from the analysis. Counts were normalized by area and a neuropil-to-somata ratio was computed for each slice. The mean neuropil-to-somata ratio was calculated for somata-translation-up target genes. All neuropil-to-somata ratios were divided by this average.

Sequencing adapters were trimmed using the Cutadapt software version 1.15 (63) with the following arguments: cut 1minimum-length 22 discard-untrimmed overlap 3 -e 0.2. An extended unique molecular identifier (UMI) was constructed from the two random nucleotides (nts) of the reverse transcription primer and the five random nucleotides of the linker and added to the FASTQ description line using a custom Perl script. To remove reads originating from noncoding RNA (ncRNA, i.e., rRNA), trimmed reads were aligned to rat ncRNA using Bowtie2 version 2.3.5.1 (very-sensitive) (64) and aligned reads were discarded. The remaining reads were aligned to the rat genome (rn6) with the split-aware aligner STAR version 2.7.3.a (65) with the following arguments: twopassMode Basic twopass1readsN -1 seedSearchStartLmax 15 outSJfilterOverhangMin 15 8 8 8 outFilterMismatchNoverReadLmax 0.1. To retrieve transcript coordinates, STARs quant mode (quantMode) was used. Throughout the study, genome alignments were used for differential expression analyses and genomic feature analyses. Transcriptome alignments were used for all other analyses. The STAR genome index was built using annotation downloaded from the University of California Santa Cruz (UCSC) table browser (66). PCR duplicates were suppressed using a custom Perl script and alignments flagged as secondary alignment were discarded before analysis. Only footprints with sizes between 24 and 34 nts were used for analyses.

Sequencing adapters and low-quality nucleotides were trimmed using the Cutadapt software version 1.15 (63) with the following arguments: minimum-length 25nextseq-trim = 20. The trimmed reads were aligned to the rat (rn6) or the mouse (mm10) genome with STAR version 2.7.3a (65).

The coordinates of genomic features (CDS, 3 UTR, 5 UTR, intron) were downloaded from the UCSC table browser in BED format (66). Bedtools version 2.26.0 (67) was used to convert BAM into BED files and to identify reads overlapping with the individual features.

P-site offsets were defined for different footprint lengths. Each footprint start position defined the footprint frame in reference to the annotated start codon. The footprint reads were virtually back projected over the start codon and the offsets from the start and the end of the read were calculated. We used every read of a given length and accumulated the most probable offset and frame. Next, the P-site position per footprint read was deduced from its length and the previously determined offset. All P-site positions were plotted for 100 nucleotides around the start and stop codons, and the center of a transcript. To correct for differences in translation rates between genes, the P-site coverage of each gene was normalized to its mean footprint coverage. The nucleotide coverage at the 0, 1, and 2 frame positions were assessed. A one-way analysis of variance (ANOVA) was used to determine if the observed frame fraction was different from the expected frame fraction. A significant P value rejected the null hypothesis that all frames featured the expected P-site coverage.

Footprint alignments were converted into the BedGraph file format using Bedtools version 2.26.0 and visualized as custom tracks on the UCSC Genome Browser (68). Footprint coverages were corrected for sequencing depth.

For both total RNA sequencing and ribosome footprint libraries from the somata and neuropil, the software featureCounts version 2.0.0 (69) was used to calculate counts per gene from reads that were aligned to the rat genome. All annotated transcript isoforms were considered. Raw counts were fed into DESeq2 version 1.30.1 and log fold change (LFC) shrinkage was used (18). Only genes with an adjusted P value are displayed in Fig. 1B.

The software featureCounts version 2.0.0 (69) was used to calculate counts per gene from reads mapped to the genome (mm10, rn6). All annotated transcript isoforms were considered. Raw counts were fed into DESeq2 version 1.30.1 and LFC shrinkage was used (18).

Gene ontology analysis was performed for neuropil- and somata-translation-up genes. All detected genes (baseMean greater than zero and with an adjusted P value), without the contaminants, were used as background. GO enrichment analysis was performed for the complete cellular component annotation using the PANTHER overrepresentation test (70, 71). The Fisher exact test was used and only GO terms with a false discovery rate (FDR) smaller than 0.05 were considered. The most specific GO terms per branch were retained. The top five GO terms with the highest enrichment scores were visualized.

Gene ontology analysis was performed for uORF-containing transcripts. All detected genes in the neuropil and the somata (baseMean greater than zero), without the contaminants, were used as background. GO enrichment analysis was performed for the complete biological process annotation using the PANTHER overrepresentation test (70, 71). The Fisher exact test was used and only GO terms with an FDR smaller than 0.05 were considered. The most specific GO terms per branch were retained. All significant GO terms were visualized.

Gene ontology analysis was performed for TEhigh and TElow transcripts. All detected genes in the neuropil and the somata (baseMean greater than zero), without the contaminants, were used as background. GO enrichment analysis was performed for the complete cellular component annotation using the PANTHER overrepresentation test (70, 71). Only GO terms with at least 50 genes in the background set were used in the analysis. The Fisher exact test was used and only GO terms with an FDR smaller than 0.05 were considered. The most specific GO terms per branch were retained. All significant GO terms were visualized.

The number of ribosomes per transcript was estimated by integrating Ribo-seq and RNA-seq libraries to calculate TE values in the neuropil. Raw Ribo-seq and RNA-seq counts, falling into gene CDS, were fed into DESeq2 version 1.30.1 and LFC shrinkage was used (18). TE values that were either significantly higher than log2(1.5) in the neuropil and the somata or smaller than log2(1.5) in the neuropil and the somata were assigned to TEhigh and TElow, respectively [lfcThreshold = log2(1.5) with an FDR < 0.05]. Only genes with a baseMean greater than 10 in the neuropil and the somata were considered. An interaction term was added to the experimental design to compare TE values between the neuropil and the somata (72).

Genes known to be associated with autism spectrum disorders were downloaded from the SFARI Gene database (https://www.sfari.org). Human gene symbols were converted into rat gene symbols. Genes with an SFARI score of 1 and 2 were considered as autism genes.

RBP motifs (human, rat, and mouse) were downloaded as position weighted matrices from the public ATtRACT database (31). The FIMO tool from the MEME suite version 5.1.1 was used to scan 5 and 3 UTRs for motif occurrences, using the default threshold (P value = 1e-4) and a precalculated nucleotide background model derived from query sequences (73). Only genes with an RBP motif occurrence were considered for analysis. For each identified RBP motif, the motif-containing genes were grouped and a median TE value was calculated. A Wilcoxon rank sum test was conducted to test if the median TE of a given RBP motif group differed from the median TE of all genes that do not contain the motif.

The ORF-RATER pipeline (https://github.com/alexfields/ORF-RATER) was run as previously described (39), starting with the harringtonine 150 s as well as the neuropil and somata BAM files. Note, that it is possible that a translated uORF may be assigned a low score, as ORF-RATER is tuned to indicate the highest-confidence sites of translation, at the expense of an increased false negative rate (74). The following parameters were used: --codons NTG for ORF types, --minrdlen 28 --maxrdlen 34 for harringtonine-treated samples, --minrdlen 27 --maxrdlen 34 for neuropil and somata samples. Only uORFs with a score of at least 0.7, a length of at least three codons, and at least one count in each of the neuropil and the somata replicates were considered.

The ribosome density of a uORF or CDS was computed as the number of ribosome footprints divided by the uORF or CDS length, respectively. The relative ribosome density was computed as the uORF ribosome density divided by the CDS ribosome density.

The 5 and 3 UTR lengths were calculated based on the Rattus norvegicus annotation version 6 (rn6). The 3 UTR lengths were corrected in accordance with newly identified 3 UTR isoforms described in ref. 12. For genes with multiple 5 UTR isoforms the longest 5 UTR sequence was chosen, giving priority to curated isoforms. For genes with multiple 3 UTRs, the most-expressed 3 UTR isoform was chosen (12).

For the comparison of 5 UTR lengths between TEhigh, TElow, and others only 5 UTRs with a minimum length of 10 nts and a maximum length of 5,000 nts were considered. For the comparison of 3 UTR lengths between TEhigh, TElow, and others, only 3 UTRs with a minimum length of 50 nts and a maximum length of 10,000 nts were considered.

For the comparison of 3 UTR lengths between neuropil-translation-up and somata-translation-up genes, the 3 UTR isoform with the highest expression in the hippocampus per gene family was considered (12).

The coverage of each gene was projected along the CDS in transcript coordinates (only exons). Genes with CDS lengths shorter than 440 codons were omitted from analysis. Each metagene profile was scaled by the average coverage between codon 400 and 20 codons before the stop codon. For each time point, the metagene profiles were smoothed with a running average window of 30 codons. For each group, the coverage tracks were accumulated, averaged, and normalized to the 0-s condition. A baseline coverage track was defined as 85% of the nontreated sample coverage track. The first positive crossing between the harringtonine-treated coverage track and the baseline coverage track determined the crossing position in codons. Elongation rates were calculated as the slope of a linear regression between the harringtonine incubation times for each track and the crossing position in codons.

Statistical significance and the tests performed are indicated in the figure legends. Statistical analysis was performed using MATLAB and R.

Details about data availability can be found in SI Appendix, Table S1. The accession number for the raw sequencing data published previously in ref. 16 is National Center for Biotechnology Information (NCBI) BioProject: PRJNA550323. The accession number for the raw sequencing data reported in this paper is NCBI BioProject: PRJNA634994. All bioinformatic tools used in this study are contained in one modular C++ program called RiboTools. The source code and further notes on the algorithms can be found on our GitHub repository (DOI: 10.5281/zenodo.3579508). Other analysis scripts and codes are available upon request.

We thank Elena Ciirdaeva for help with mRNA library preparation. A.B. is supported by a European Molecular Biology Organization (EMBO) long-term postdoctoral fellowship (EMBO ALTF 331-2017). E.M.S. is funded by the Max Planck Society, an Advanced Investigator award from the European Research Council (Grant 743216), Deutsche Forschungsgemeinschaft (DFG) Collaborative Research Centre (CRC) 1080: Molecular and Cellular Mechanisms of Neural Homeostasis, and DFG CRC 902: Molecular Principles of RNA-Based Regulation.

Author contributions: C.G., A.B., and E.M.S. designed research; C.G., A.B., B.N.-A., A.K., I.B., and S.t.D. performed research; C.G., A.B., and G.T. analyzed data; and A.B. and E.M.S. wrote the paper.

Reviewers: C.M.A., The University of Edinburgh Centre for Genomic and Experimental Medicine; and E.K., New York University.

The authors declare no competing interest.

See online for related content such as Commentaries.

This article contains supporting information online at https://www.pnas.org/lookup/suppl/doi:10.1073/pnas.2113929118/-/DCSupplemental.

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The translatome of neuronal cell bodies, dendrites, and axons - pnas.org

Virtual Care Market to Witness Exponential Growth by 2031 – BioSpace

Global Virtual Care Market: Snapshot

Virtual care refers to a technique that allows for the treatment of patients dealing with different health issues with the help of advanced technologies such as audio, video, or written communication. Moreover, it also includes virtual visits performed using communication devices held by patients as well as physicians from diverse places.

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The report analysts have performed segmentation of the global virtual care market on the basis of several important parameters such as consultation type, end-user, and region. On the basis of consultation type, the market is classified into audio consultation, kiosks, and video consultation.

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Key Drivers of Virtual Care Market Growth

In the healthcare sector, there is notable growth in the application of different advanced technologies such as virtual care owing to the flexibility provided by the connected devices. Moreover, people today are inclining toward the use of virtual care services as they get an opportunity to gain second opinions from qualified healthcare professionals through online channels.

Virtual care is utilized by patients for performing varied activities such as consultations, meetings, check-ins, and checking the status of their reports. In addition, this technique can be utilized in the management of diseases that need continual follow ups. Thus, increased number of individuals suffering from critical health issues such as hypertension and diabetes is expected to support in the rapid expansion of the global virtual care market in the years to come.

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Virtual care solutions are increasingly adopted across major parts of the globe as they offer a wide range of advantages such as accessibility to doctors or healthcare providers with the help of video conferencing, which can be a prominent option in case of medical emergencies in remote areas.

The virtual care technique is adopted by healthcare specialists as they can focus on critical cases, as the technology gives them direct access to the patient medication room or to the hospital even if they are not physically present at that particular place.

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What Key Strategies are Utilized by Companies in Global Virtual Care Market to Stay Ahead in Competition

The global virtual care market is fragmented in nature and its competitive landscape is highly intense. Players are utilizing diverse strategies to maintain their prominent market positions. Some of the key strategies utilized by market enterprises are partnerships, collaborations, and mergers and acquisitions.

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North America Demand Outlook for Virtual Care

In terms of region, the global virtual care market shows existence in many regions such as Europe, Asia Pacific, North America, South America, and Middle East and Africa. Among all regions, North America is one of the dominant regions of the market for virtual care.

The North America virtual care market is estimated to maintain its dominant position in the forthcoming years due to early adoption of advanced technologies in the region.

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Virtual Care Market to Witness Exponential Growth by 2031 - BioSpace

The Rise of Longevity Therapeutics – Pharmaceutical Executive

Aging is the ultimate risk factor for most diseases, such as cancer, neurodegenerative, cardiovascular, diabetes, degenerative fibrosis and many others. When we are young, we are typically healthy, despite a predisposition that will lead inevitably to a specific degenerative condition. However, the degenerative processes do not kick in until a certain age, when we are older. It looks like when we are younger, the body can compensate cumulative stress and damage caused to our cells in the tissues, allowing to maintain that equilibrium, called homeostasis, that keeps our organs functional and healthy. However, over time this buffering capacity becomes thinner and thinner, until things wear off: our tissues stop working as they used to. These changes are typically caused by an initial small number of rare but bad cells, that progressively increase over time, causing additional damage to the good cells that eventually stop working efficiently, causing a vicious cycle. Eventually the bad cells take over leading to the onset of a disease.

Our body is equipped with a number or regenerative and healing functions. Some are intrinsic in every cell, such as DNA repair mechanisms that are triggered when something compromises the integrity of our genomic structures. These are important functions that enable a cell, for example when it replicates, to repair errors and other damages that might have happened to our DNA. For example, two large proteins called ATM and ATR, involved in the cellular response to DNA damage, are responsible to maintain genomic instability caused by intrinsic and external DNA-damaging agents, such as UV light or various chemicals and toxins. A lack of functions of these proteins results in progressive neurodegeneration, immunodeficiency, predisposition to malignancy or radiation sensitivity. Mutations on the genes encoding these proteins can cause premature aging and premature development of these diseases, but this occurs also naturally, over time.

Cells also have an intrinsic immune system, producing factors called interferons employed by the cells as antiviral agents and to modulate other immune functions. It can be triggers by a viral infection so when a cell is infected will release interferons, protecting the neighbor cells against potential infection. Interferons can also suppress growth of blood vessels preventing tumors to get nutrients and growing. They can also activate immune cells so they can better fight viruses, tumors and others agents. Unfortunately, an age-related decline or impaired innate interferon functions in the cells results in a number of negative consequences in the body, such as increased susceptibility of the elderly to infections, tumors and damage.

In the body there are several cell types responsible to keep the tissues in check. The immune system is specialized to recognize remove and remember damaging agents. Those could be external, such as virus, bacteria or parasites, or internal, such as tumorigenic cells or senescent cells (see below). The immune system is a very sophisticated network of cell types, intercommunicating with each other to maintain the body clean from damaging factors. As we age the immune system also ages and loses capacity to recognize or responding to these damaging agents. It also become exhausted by an increasing chronic inflammation that progressively accumulate as we age, phenomenon also called inflammaging.

Another important repairing mechanism is the regenerative tissue functions, driven by the stem cells. Those cells are progenitor cells, often dormant in a quiescent state in the tissue and waiting to be activated by some damage. Stem cells are critical because once activated they can generate a progeny of daughter cells capable of re-growing the damaged tissue back to its original structure and function. Stem cells have another important function: they can regenerate themselves, in a process called self-renewal. This is important so that the new repaired tissue can repeat the process if a new damage occurs. The regenerative capacity of our body is remarkable, allowing our tissues to keep their integrity, health and functions. However, over time also stem cells age or respond to the aged microenvironment where they live (called the niche), and they become less efficient to repair tissues or to self-renewing. As a result, our tissues change, become atrophic, fibrotic or dysfunctional leading eventually to diseases.

In regenerative medicine, the application of stem cells resulted of the generation of multiple new therapeutic opportunities. A promising area uses stem cells to generate bioengineering strategies to grow new tissues in a petri dish to be then transplanted in the body to repair damaged tissues. Some applications are already in clinical use, such as for skin grafts. Many others are on their way, either in preclinical development or in clinical trials for many different tissue types and for different clinical indications.

Another promising stem cells application is the direct transplantation into damaged tissues, where they can grow and engraft repairing. However, as we age stem cells become less efficient. What if we If we could rejuvenate them? We could restore their capacity to repair our tissues and maintain homeostasis. Promising and exciting strategies are advancing in that direction. For example, we and others showed that it is possible to reprogram epigenetically a cell so it can become the younger and healthier version of itself (Sarkar et al., 2020). This is a mechanism that every cell has encoded in its DNA, but normally works only in the germline (the sperm and the egg) during the embryogenesis to make sure that the cellular clock is turned back to zero, before initiating the cellular programs to generate the embryo. This important for example to prevent making old newborn babies. This intrinsic rejuvenative mechanism is locked in the other somatic cells of the body. We found it is possible to re-activate it transiently and safely, without changing the identity of the cell, enabling to push back the cellular clock of aged human cells to make them healthier and restore their functions. These technologies are under development to be translated into therapeutics with the promise that one day could rejuvenate the aged cells in the body so they can become the younger version of themselves, repeating the process over time when needed.

Among many of the drivers of the aging process, there is one that seems to stands out as the lower hanging fruit among the emerging space of the longevity therapeutics. This is cellular senescence. Every damage that occurs to the cells in our body can push the cells to stop what they are doing and activate a safety mechanism that locks them into an arrested state called cellular senescence. Senescent cells cannot replicate anymore preventing them to cause additional damage, such as becoming cancer cells. All sort of damage can trigger this response leading to cellular senescence such as, oxidative stress, mitochondrial dysfunctions, DNA damage, viral infection, cigarette smoking, pollutions, chemicals, etc. They all can induce that safety lock and push damage cells to become senescent.

Senescent cells dont die easily but they stick around in the tissue, accumulating slowly over time. Importantly, cellular senescence is a pleiotropic mechanism, meaning it can be both good or bad. When we are young, we can efficiently get rid of senescent cells. The body uses them positively such as for tissue repair, wound healing or tissue remodeling. However, as we age, and our immune system ages (partially trough cellular senescence, a phenomenon called immune-senescence), our body become less efficient in removing senescent cells, which then start to accumulate.

Being able to make a new generation of drugs that are very selective for senescent cells, will enable the promise to achieve rejuvenative clinical results in humans similarly to what we found in preclinical results. On that end, we recently published a targeted strategy with the goal to advance the field in that direction (Doan et al., 2020). Using a prodrug, we engineered a small molecule to generate a selective senolytic compound to develop a targeted therapy. This prodrug is inactive in non-senescent cells but activated by senescent cells, taking advantage of an enzymatic function of those cells. In geriatric mice this prodrug showed to be well tolerated but also efficacious to clear senescent cells, resulting in restored cognitive functions, muscle functions, stem cells functions, vitality and overall health. As we advance senolytic drugs to the clinic to treat age-related diseases, it is very important to be mindful that elderly individuals, who are frail, with co-morbidities and exposed to multiple medications, will not well tolerate drugs that are not safe and effective. Importantly, not all senescent cells are the same. They are rare, interspersed in the tissues but are also very heterogeneous. Being able to hit the right senescent cells, in the right diseased tissue will be key to enable effective therapies. Developing drugs that are very potent, selective and potent and safe will be pivotal.

The longevity therapeutics space is emerging, but is already disrupting the medical industry. The goal of longevity therapeutics is not just to add years to life, extending lifespan. The true goal is to add life to years and extend health span. A target that gets closer every day.

Marco Quarta is CEO, Rubedo Life Sciences.

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The Rise of Longevity Therapeutics - Pharmaceutical Executive

Tooth Regeneration Market Size | COVID-19 Impact Analysis | Forecast to 2027 investigated in the latest research – WhaTech

Tooth Regeneration Market Global Trends, Market Share, Industry Size, Growth, Opportunities and Market Forecast - 2021 to 2027. The tooth regeneration market size is expected to grow significantly from 2021 to 2027.

The tooth regeneration marketsize is expected to grow significantly from 2021 to 2027.

Tooth regeneration is a stem cell-based regenerative medical procedure used in the fields of tissue engineering and stem cell biology. The tooth regeneration procedure replaces damaged or lost teeth by growing on autologous stem cells.

Somatic cells are collected and reprogrammed to derive pluripotent stem cells and tooth layers with the help of resorbable biopolymers.

(Getthis Report)

A full report of Global Tooth Regeneration Market is available at: http://www.orionmarketreports.com/tooth-rket/55394/

Market Segments

By Type

By Process

By End-use

Key Players

Key players operating in the global tooth regeneration market include Unilever, Ocata Therapeutics, Integra LifeSciences, CryoLife, Inc., BioMimetic Therapeutics, Inc. (Wright Medical Group, Inc.), Cook Medical, and StemCells Inc.

Scope of the Report

The research study analyzes the global Tooth Regeneration industry from 360-degree analysis of the market thoroughly delivering insights into the market for better business decisions, considering multiple aspects some of which are listed below as:

Recent Developments

o Market Overview and growth analysis o Import and Export Overview o Volume Analysis o Current Market Trends and Future Outlook o Market Opportunistic and Attractive Investment Segment

Geographic Coverage

o North America Market Size and/or Volume o Latin America Market Size and/or Volume o Europe Market Size and/or Volume o Asia-Pacific Market Size and/or Volume o Rest of the world Market Size and/or Volume

Key Questions Answered by Tooth Regeneration Market Report

1. What was the Tooth Regeneration Market size in 2019 and 2020; what are the estimated growth trends and market forecast (2021-2027).

2. What will be the CAGR of the Tooth Regeneration Market during the forecast period (2021-2027)?

3. Which segments (product type/applications/end-user) were most attractive for investments in 2021? How these segments are expected to grow during the forecast period (2021-2027).

4. Which manufacturer/vendor/players in the Tooth Regeneration Market was the market leader in 2020?

5. Overview on the existing product portfolio, products in the pipeline, and strategic initiatives taken by key vendors in the market.

The report covers the following objectives:

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Tooth Regeneration Market Size | COVID-19 Impact Analysis | Forecast to 2027 investigated in the latest research - WhaTech

Global Induced Pluripotent Stem Cell (iPS Cell) Market Report 2021: In Total, At Least 68 Distinct Market Competitors Now Offer Various Types of iPSC…

DUBLIN, April 15, 2021 /PRNewswire/ -- The "Global Induced Pluripotent Stem Cell (iPS Cell) Industry Report 2021" report has been added to ResearchAndMarkets.com's offering.

The main objectives of this report are to describe the current status of iPSC research, patents, funding events, industry partnerships, biomedical applications, technologies, and clinical trials for the development of iPSC-based therapeutics.

Since the discovery of induced pluripotent stem cells (iPSCs) a large and thriving research product market has grown into existence, largely because the cells are non-controversial and can be generated directly from adult cells. It is clear that iPSCs represent a lucrative market segment because methods for commercializing this cell type are expanding every year and clinical studies investigating iPSCs are swelling in number.

Therapeutic applications of iPSCs have surged in recent years. 2013 was a landmark year in Japan because it saw the first cellular therapy involving the transplant of iPSCs into humans initiated at the RIKEN Center in Kobe, Japan. Led by Masayo Takahashi of the RIKEN Center for Developmental Biology (CDB), it investigated the safety of iPSC-derived cell sheets in patients with macular degeneration.

In another world-first, Cynata Therapeutics received approval in 2016 to launch the world's first formal clinical trial of an allogeneic iPSC-derived cell product (CYP-001) for the treatment of GvHD.

Riding the momentum within the CAR-T field, Fate Therapeutics is developing FT819, its "off-the-shelf" iPSC-derived CAR-T cell product candidate. Numerous physician-led studies using iPSCs are also underway in Japan, a leading country for basic and applied iPSC applications.

iPS Cell Market Competitors

Today, FUJIFILM CDI has emerged as one of the largest commercial players within the iPSC sector. FUJIFILM CDI was founded in 2004 by Dr. James Thomson at the University of Wisconsin-Madison, who in 2007 derived iPSC lines from human somatic cells for the first time ever. The feat was accomplished simultaneously by Dr. Shinya Yamanaka's lab in Japan.

In 2009, ReproCELL, a company established as a venture company originating from the University of Tokyo and Kyoto University, made iPSC products commercially available for the first time with the launch of its human iPSC-derived cardiomyocytes, which it called ReproCario.

A European leader within the iPSC market is Ncardia, formed through the merger of Axiogenesis and Pluriomics. Founded in 2001, Axiogenesis initially focused on generating mouse embryonic stem cell-derived cells and assays, but after Yamanaka's iPSC technology became available, it became the first European company to license it in 2010. Ncardia's focus is on preclinical drug discovery and drug safety through the development of functional assays using human neuronal and cardiac cells.

In total, at least 68 distinct market competitors now offer various types of iPSC products, services, technologies and therapies.

iPS Cell Commercialization

Methods of commercializing induced pluripotent stem cells (iPSCs) are diverse and continue to expand. iPSC cell applications include, but are not limited to:

Since the discovery of iPSC technology in 2006, significant progress has been made in stem cell biology and regenerative medicine. New pathological mechanisms have been identified and explained, new drugs identified by iPSC screens are in the pipeline, and the first clinical trials employing human iPSC-derived cell types have been initiated.

Key Topics Covered:

1. REPORT OVERVIEW 1.1 Statement of the Report 1.2 Executive Summary

2. INTRODUCTION 2.1 Discovery of iPSCs 2.2 Barriers in iPSC Application 2.3 Timeline and Cost of iPSC Development 2.4 Current Status of iPSCs Industry 2.4.1 Share of iPSC-based Research within the Overall Stem Cell Industry 2.4.2 Major Focuses of iPSC Companies 2.4.3 Commercially Available iPSC-Derived Cell Types 2.4.4 Relative Use of iPSC-Derived Cell Types in Toxicology Testing Assays 2.4.5 Toxicology/Safety Testing Assays using iPSC-Derived Cell Types 2.5 Currently Available iPSC Technologies 2.6 Advantages and Limitations of iPSCs Technology

3. HISTORY OF INDUCED PLURIPOTENT STEM CELLS (IPSCS) 3.1 First iPSC generation from Mouse Fibroblasts, 2006 3.2 First Human iPSC Generation, 2007 3.3 Creation of CiRA, 2010 3.4 First High-Throughput screening using iPSCs, 2012 3.5 First iPSCs Clinical Trial Approved in Japan, 2013 3.6 The First iPSC-RPE Cell Sheet Transplantation for AMD, 2014 3.7 EBiSC Founded, 2014 3.8 First Clinical Trial using Allogeneic iPSCs for AMD, 2017 3.9 Clinical Trials for Parkinson's disease using Allogeneic iPSCs, 2018 3.10 Commercial iPSC Plant SMaRT Established, 2018 3.11 First iPSC Therapy Center in Japan, 2019

4. RESEARCH PUBLICATIONS ON iPSCS 4.1 Categories of Research Publications 4.2 Percent Share of Published Articles by Disease Type 4.3 Number of Articles by Country

5. IPSCS: PATENT LANDSCAPE 5.1 Timeline and Status of Patents 5.2 Patent Filing Destinations 5.2.1 Patent Applicant's Origin 5.2.2 Top Ten Global Patent Applicants 5.2.3 Collaborating Applicants of Patents 5.3 Patent Application Trends iPSC Preparation Technologies 5.4 Patent Application Trends in iPSC Differentiation Technologies 5.5 Patent Application Trends in Disease-Specific Cell Technologies

6. CLINICAL TRIALS INVOLVING iPSCS 6.1 Current Clinical Trials Landscape 6.1.1 Clinical Trials Involving iPSCs by Major Diseases 6.1.2 Clinical Trials Involving iPSCs by Country

7. FUNDING FOR iPSCs 7.1 Value of NIH Funding for iPSCs 7.1.1 NHI's Intended Funding Through its Component Organizations in 2020 7.1.2 NIH Funding for Select Universities for iPSC Studies 7.2 CIRM Funding for iPSCs

8. GENERATION OF INDUCED PLURIPOTENT STEM CELLS: AN OVERVIEW 8.1 Reprogramming Factors 8.2 Overview of Four Key Methods of Gene Delivery 8.3 Comparative Effectiveness of Different Vector Types 8.4 Genome Editing Technologies in iPSCs Generation

9. HUMAN iPSC BANKING 9.1 Cell Sources for iPSCs Banking 9.2 Reprogramming methods used in iPSC Banking 9.3 Workflow in iPSC Banks 9.4 Existing iPSC Banks

10. BIOMEDICAL APPLICATIONS OF iPSCS 10.1 iPSCs in Basic Research 10.2 iPSCs in Drug Discovery 10.3 iPSCs in Toxicology Studies 10.4 iPSCs in Disease Modeling 10.5 iPSCs within Cell-Based Therapies

11. OTHER NOVEL APPLICATIONS OF iPSCS 11.1 iPSCs in Tissue Engineering 11.2 iPSCs in Animal Conservation 11.3 iPSCs and Cultured Meat

12. DEAL-MAKING WITHIN THE iPSC SECTOR 12.1 License Agreement between FUJIFILM Cellular Dynamics and Sana 12.2 Century Therapeutics Closes $160 Million Series C Financing 12.3 Bluerock Gains Access to Ncardia's iPSCs-derived Cardiomyocytes 12.4 Fate Therapeutics' Deal with Janssen Biotech 12.5 Century Therapeutics Acquires Empirica Therapeutics 12.6 $250 Million Raised by Century Theraputics 12.7 BlueRock Therapeutics Launched with $225 Million 12.8 Collaboration between Allogene Therapeutics and Notch Therapeutics 12.9 Acquisition of Semma Therapeutics by Vertex Therapeutics 12.10 Evotec's Extended Collaboration with BMS 12.11 Licensing Agreement between Allele Biotechnology and Astellas 12.12 Codevelopment Agreement between Allele & SCM Lifesciences 12.13 Fate Therapeutics Signs $100 Million Deal with Janssen 12.14 Allele's Deal with Alpine Biotherapeutics 12.15 Editas and BlueRock's Development Agreement

13. MARKET OVERVIEW 13.1 Global Market for iPSCs by Geography 13.2 Global Market for iPSCs by Technology 13.3 Global Market for iPSCs by Biomedical Application 13.4 Global Market for iPSCs by Cell Types 3.5 Market Drivers 13.6 Market Restraints

14. COMPANY PROFILES

For more information about this report visit https://www.researchandmarkets.com/r/u155vh

Media Contact:

Research and Markets Laura Wood, Senior Manager [emailprotected]

For E.S.T Office Hours Call +1-917-300-0470 For U.S./CAN Toll Free Call +1-800-526-8630 For GMT Office Hours Call +353-1-416-8900

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Global Induced Pluripotent Stem Cell (iPS Cell) Market Report 2021: In Total, At Least 68 Distinct Market Competitors Now Offer Various Types of iPSC...

Emergent drift of Dental Regenerative Products Market By the World in Upcoming Year 2021-2027 with Leading Key Players: Provia Laboratories, LLC,…

Stem cell-based regenerative medicine procedure replaces lost or damaged teeth in tissue engineering and stem cell biology by redrawing from autologous stem cells called tooth regeneration. Somatic stem cells are collected and reprogrammed into induced pluripotent stem cells as a source of new bioengineered teeth, placed in a reabsorbable biopolymer in the shape of a new tooth or directly in the dental plate.

The Global Dental Regenerative Products market elaborate report, offers a summary study on regional forecast, business size, and associated revenue estimations. The Dental Regenerative Products report more emphasizes primary challenges and growth trends adopted by leading makers of the market.

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Top Companies of Dental Regenerative Products Market:

Dental Regenerative Products Market Is Divided Into:

Based on end user, the dental regenerative products market can be segmented as:

The report provides a detailed breakdown of the Dental Regenerative Products Market region-wise and categorizes it at various levels. Regional segment analysis displaying regional production volume, consumption volume, revenue, and growth rate from 2019-2027 covers: Americas (United States, Canada, Mexico, Brazil), APAC (China, Japan, Korea, Southeast Asia, India, Australia), Europe (Germany, France, UK, Italy, Russia, Spain), Middle East & Africa (Egypt, South Africa, Israel, Turkey, GCC Countries)

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This Dental Regenerative Products Market research report also presents some significant practical oriented case studies which help to understand the subject matter clearly. This research report has been prepared through industry analysis techniques and presented in a professional manner by including effective info-graphics whenever necessary. It helps to gain stability in the businesses as well as to make the rapid developments to achieve a notable remark in the Global market space.

In This Study, The Years Considered To Estimate The Size Of Dental Regenerative Products Market Are As Follows:

History Year: 2015-2020

Base Year: 2020

Estimated Year: 2021

Forecast Year 2021 to 2027

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Emergent drift of Dental Regenerative Products Market By the World in Upcoming Year 2021-2027 with Leading Key Players: Provia Laboratories, LLC,...

Worldwide Cell Therapy Industry to 2027 – Increasing Prevalence of Chronic Diseases is Driving the Market – PRNewswire

DUBLIN, April 1, 2021 /PRNewswire/ -- The "Cell Therapy Market Forecast to 2027 - COVID-19 Impact and Global Analysis By Therapy Type; Product; Technology; Application; End User, and Geography" report has been added to ResearchAndMarkets.com's offering.

According to this report the global cell therapy market is expected to reach US$ 12,563.23 million by 2027 from US$ 7,260.50 million in 2019. It is estimated to grow at a CAGR of 7.2% from 2020-2027. The growth of the market is attributed to increasing prevalence of chronic diseases, rising adoption of regenerative medicines, and surging number of approvals for cell-based therapies. However, the high cost of cell therapy manufacturing hinders the growth of the market.

The cell therapy market, based on therapy type, is bifurcated into allogeneic and autologous. In 2019, the allogeneic segment accounted for a larger share owing to the availability of substantial number of approved products for clinical use. For instance, in 2018, Alofisel developed by TiGenix (Takeda) is the first allogeneic stem cell-based therapy approved for use in Europe.

Chronic diseases, such as cardiovascular disorders, neurological disorders, autoimmune disorders, and cancer, are the leading causes of death and disability worldwide. As per the Centers for Disease Control and Prevention (CDC), in 2019, nearly 6 in 10 people suffered from at least one chronic disease in the US. Cardiovascular diseases (CVDs) are a significant cause of mortality owing to the hectic lifestyle. As per the World Health Organization (WHO), CVDs are the number 1 cause of death globally, taking an estimated 17.9 million lives each year. Cancer is among the leading causes of mortality worldwide, and the disease affects a huge population; therefore, it acts as a huge financial burden on society. According to the WHO, in 2018, ~9.6 million deaths occurred due to cancer globally. However, growing research on developing effective treatments for the disease is positively affecting the market growth. Gene therapy and cell therapy are transforming the cancer treatment landscape; for example, Novartis Kymriah is used to treat diffuse large B-cell lymphoma. The launches of more such products would be driving the demand for cell therapy, thus driving the growth of the cell therapy market in the coming years.

The COVID-19 outbreak was first reported in Wuhan (China) in December 2019. The pandemic is causing massive disruptions in supply chains, consumer markets, and economy across the world. As the healthcare sector is focusing on saving lives of COVID-19 patients, the demand for cell therapy is reducing worldwide.

Vericel Corporation; MEDIPOST; NuVasive, Inc.; Mesoblast Limited; JCR Pharmaceuticals Co. Ltd.; Smith & Nephew; Bristol-Myers Squibb Company; Cells for Cells; Stemedica Cell Technologies, Inc; and Castle Creek Biosciences, Inc. are among the companies operating in the cell therapy market.

Reasons to Buy

Key Topics Covered:

1. Introduction 1.1 Scope of the Study 1.2 Research Report Guidance 1.3 Market Segmentation 1.3.1 Global Cell Therapy Market - By Therapy Type 1.3.2 Global Cell Therapy Market - By Product 1.3.3 Global Cell Therapy Market - By Technology 1.3.4 Global Cell Therapy Market - By Application 1.3.5 Global Cell Therapy Market - By End User 1.3.6 Global Cell Therapy Market - By Geography

2. Cell Therapy Market - Key Takeaways

3. Research Methodology 3.1 Coverage 3.2 Secondary Research 3.3 Primary Research

4. Global Cell therapy- Market Landscape 4.1 Overview 4.2 PEST Analysis 4.2.1 North America - PEST Analysis 4.2.2 Europe- PEST Analysis 4.2.3 Asia Pacific- PEST Analysis 4.2.4 Middle East and Africa - PEST Analysis 4.2.5 South and Central America - PEST Analysis 4.3 Expert Opinions

5. Global Cell Therapy Market - Key Industry Dynamics 5.1 Key Market Drivers 5.1.1 Increasing Prevalence of Chronic Diseases 5.1.2 Rising Adoption of Regenerative Medicines 5.1.3 Increasing Number of Approvals for Cell-Based Therapies 5.2 Key Market Restraints 5.2.1 High Cost of Cell Therapy Manufacturing 5.3 Key Market Opportunities 5.3.1 Increasing Adoption of Cell Therapy in Developing Regions 5.4 Future Trends 5.4.1 Shift Toward Automated Cell Therapy Manufacturing 5.5 Impact Analysis of Drivers and Restraints

6. Cell therapy Market - Global Analysis 6.1 Global Cell therapy Market Revenue Forecast And Analysis 6.2 Global Cell therapy Market, By Geography - Forecast And Analysis 6.3 Market Positioning

7. Cell therapy Market Analysis - By Therapy Type 7.1 Overview 7.2 Cell therapy Market Revenue Share, by Therapy Type (2019 and 2027) 7.3 Allogeneic 7.3.1 Overview 7.3.2 Allogeneic: Cell therapy Market - Revenue and Forecast to 2027 (US$ Million) 7.4 Autologous 7.4.1 Overview 7.4.2 Autologous: Cell therapy Market - Revenue and Forecast to 2027 (US$ Million)

8. Cell therapy Market Analysis - By Product 8.1 Overview 8.2 Cell therapy Market Revenue Share, by Product (2019 and 2027) 8.3 Consumables 8.3.1 Overview 8.3.2 Consumables: Cell therapy Market - Revenue and Forecast to 2027 (US$ Million) 8.4 Equipment 8.4.1 Overview 8.4.2 Equipment: Cell therapy Market - Revenue and Forecast to 2027 (US$ Million) 8.5 Systems and Software 8.5.1 Overview 8.5.2 Systems and Software: Cell therapy Market - Revenue and Forecast to 2027 (US$ Million)

9. Cell therapy Market Analysis - By Technology 9.1 Overview 9.2 Cell therapy Market Revenue Share, by Technology (2019 and 2027) 9.3 Viral Vector Technology 9.3.1 Overview 9.3.2 Viral Vector Technology: Cell therapy Market - Revenue and Forecast to 2027 (US$ Million) 9.4 Genome Editing Technology 9.4.1 Overview 9.4.2 Genome Editing Technology: Cell therapy Market - Revenue and Forecast to 2027 (US$ Million) 9.5 Somatic Cell Technology 9.5.1 Overview 9.5.2 Somatic Cell Technology: Cell therapy Market - Revenue and Forecast to 2027 (US$ Million) 9.6 Cell Immortalization Technology 9.6.1 Overview 9.6.2 Cell Immortalization Technology: Cell therapy Market - Revenue and Forecast to 2027 (US$ Million) 9.7 Cell Plasticity Technology 9.7.1 Overview 9.7.2 Cell Plasticity Technology: Cell therapy Market - Revenue and Forecast to 2027 (US$ Million) 9.8 Three-Dimensional Technology 9.8.1 Overview 9.8.2 Three-Dimensional Technology: Cell therapy Market - Revenue and Forecast to 2027 (US$ Million)

10. Cell therapy Market Analysis - By Application 10.1 Overview 10.2 Cell therapy Market Revenue Share, by Application (2019 and 2027) 10.3 Oncology 10.3.1 Overview 10.3.2 Oncology: Cell therapy Market - Revenue and Forecast to 2027 (US$ Million) 10.4 Cardiovascular 10.4.1 Overview 10.4.2 Cardiovascular: Cell therapy Market - Revenue and Forecast to 2027 (US$ Million) 10.5 Orthopedic 10.5.1 Overview 10.5.2 Orthopedic: Cell therapy Market - Revenue and Forecast to 2027 (US$ Million) 10.6 Wound Management 10.6.1 Overview 10.6.2 Wound Management: Cell therapy Market - Revenue and Forecast to 2027 (US$ Million) 10.7 Other Applications 10.7.1 Overview 10.7.2 Other Applications: Cell therapy Market - Revenue and Forecast to 2027 (US$ Million)

11. Cell therapy Market Analysis - By End User 11.1 Overview 11.2 Cell therapy Market Share, by End User, 2019 and 2027, (%) 11.3 Hospitals 11.3.1 Overview 11.3.2 Hospitals: Cell therapy Market - Revenue and Forecast to 2027 (US$ Million) 11.4 Research Institutes 11.4.1 Overview 11.4.2 Research Institutes: Cell therapy Market - Revenue and Forecast to 2027 (US$ Million) 11.5 Others 11.5.1 Overview 11.5.2 Others: Cell therapy Market - Revenue and Forecast to 2027 (US$ Million)

12. Cell therapy Market - Geographic Analysis 12.1 North America: Cell Therapy Market 12.2 Europe: Cell therapy Market 12.3 Asia Pacific: Cell Therapy Market 12.4 Middle East and Africa: Cell Therapy Market 12.5 South and Central America: Cell Therapy Market

13. Impact of COVID-19 Pandemic on Global Cell Therapy Market 13.1 North America: Impact Assessment of COVID-19 Pandemic 13.2 Europe: Impact Assessment of COVID-19 Pandemic 13.3 Asia-Pacific: Impact Assessment of COVID-19 Pandemic 13.4 Middle East & Africa: Impact Assessment of COVID-19 Pandemic 13.5 South & Central America: Impact Assessment of COVID-19 Pandemic

14. Cell Therapy Market- Industry Landscape 14.1 Overview 14.2 Growth Strategies Done by the Companies in the Market, (%) 14.3 Organic Developments 14.3.1 Overview 14.4 Inorganic Developments 14.4.1 Overview

15. Company Profiles 15.1 Vericel Corporation 15.1.1 Key Facts 15.1.2 Business Description 15.1.3 Products and Services 15.1.4 Financial Overview 15.1.5 SWOT Analysis 15.1.6 Key Developments 15.2 MEDIPOST 15.2.1 Key Facts 15.2.2 Business Description 15.2.3 Products and Services 15.2.4 Financial Overview 15.2.5 SWOT Analysis 15.2.6 Key Developments 15.3 NuVasive, Inc. 15.3.1 Key Facts 15.3.2 Business Description 15.3.3 Products and Services 15.3.4 Financial Overview 15.3.5 SWOT Analysis 15.3.6 Key Developments 15.4 Mesoblast Limited 15.4.1 Key Facts 15.4.2 Business Description 15.4.3 Products and Services 15.4.4 Financial Overview 15.4.5 SWOT Analysis 15.4.6 Key Developments 15.5 JCR Pharmaceuticals Co. Ltd. 15.5.1 Key Facts 15.5.2 Business Description 15.5.3 Products and Services 15.5.4 Financial Overview 15.5.5 SWOT Analysis 15.5.6 Key Developments 15.6 Smith & Nephew 15.6.1 Key Facts 15.6.2 Business Description 15.6.3 Products and Services 15.6.4 Financial Overview 15.6.5 SWOT Analysis 15.6.6 Key Developments 15.7 Bristol-Myers Squibb Company 15.7.1 Key Facts 15.7.2 Business Description 15.7.3 Products and Services 15.7.4 Financial Overview 15.7.5 SWOT Analysis 15.7.6 Key Developments 15.8 Cells for Cells 15.8.1 Key Facts 15.8.2 Business Description 15.8.3 Products and Services 15.8.4 Financial Overview 15.8.5 SWOT Analysis 15.8.6 Key Developments 15.9 Stemedica Cell Technologies, Inc 15.9.1 Key Facts 15.9.2 Business Description 15.9.3 Products and Services 15.9.4 Financial Overview 15.9.5 SWOT Analysis 15.9.6 Key Developments 15.10 Castle Creek Biosciences, Inc. 15.10.1 Key Facts 15.10.2 Business Description 15.10.3 Products and Services 15.10.4 Financial Overview 15.10.5 SWOT Analysis 15.10.6 Key Developments

16. Appendix 16.1 About the Publisher 16.2 Glossary of Terms

For more information about this report visit https://www.researchandmarkets.com/r/hxk6k0

Media Contact:

Research and Markets Laura Wood, Senior Manager [emailprotected]

For E.S.T Office Hours Call +1-917-300-0470 For U.S./CAN Toll Free Call +1-800-526-8630 For GMT Office Hours Call +353-1-416-8900

U.S. Fax: 646-607-1907 Fax (outside U.S.): +353-1-481-1716

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Excerpt from:
Worldwide Cell Therapy Industry to 2027 - Increasing Prevalence of Chronic Diseases is Driving the Market - PRNewswire

Outlook on the Cell Therapy Global Market to 2027 – by Therapy Type, Product, Technology, Application, End-user and Geography – GlobeNewswire

Dublin, March 31, 2021 (GLOBE NEWSWIRE) -- The "Cell Therapy Market Forecast to 2027 - COVID-19 Impact and Global Analysis By Therapy Type; Product; Technology; Application; End User, and Geography" report has been added to ResearchAndMarkets.com's offering.

According to this report the global cell therapy market is expected to reach US$ 12,563.23 million by 2027 from US$ 7,260.50 million in 2019. It is estimated to grow at a CAGR of 7.2% from 2020-2027. The growth of the market is attributed to increasing prevalence of chronic diseases, rising adoption of regenerative medicines, and surging number of approvals for cell-based therapies. However, the high cost of cell therapy manufacturing hinders the growth of the market.

The cell therapy market, based on therapy type, is bifurcated into allogeneic and autologous. In 2019, the allogeneic segment accounted for a larger share owing to the availability of substantial number of approved products for clinical use. For instance, in 2018, Alofisel developed by TiGenix (Takeda) is the first allogeneic stem cell-based therapy approved for use in Europe.

Chronic diseases, such as cardiovascular disorders, neurological disorders, autoimmune disorders, and cancer, are the leading causes of death and disability worldwide. As per the Centers for Disease Control and Prevention (CDC), in 2019, nearly 6 in 10 people suffered from at least one chronic disease in the US. Cardiovascular diseases (CVDs) are a significant cause of mortality owing to the hectic lifestyle. As per the World Health Organization (WHO), CVDs are the number 1 cause of death globally, taking an estimated 17.9 million lives each year. Cancer is among the leading causes of mortality worldwide, and the disease affects a huge population; therefore, it acts as a huge financial burden on society. According to the WHO, in 2018, ~9.6 million deaths occurred due to cancer globally. However, growing research on developing effective treatments for the disease is positively affecting the market growth. Gene therapy and cell therapy are transforming the cancer treatment landscape; for example, Novartis Kymriah is used to treat diffuse large B-cell lymphoma. The launches of more such products would be driving the demand for cell therapy, thus driving the growth of the cell therapy market in the coming years.

The COVID-19 outbreak was first reported in Wuhan (China) in December 2019. The pandemic is causing massive disruptions in supply chains, consumer markets, and economy across the world. As the healthcare sector is focusing on saving lives of COVID-19 patients, the demand for cell therapy is reducing worldwide.

Vericel Corporation; MEDIPOST; NuVasive, Inc.; Mesoblast Limited; JCR Pharmaceuticals Co. Ltd.; Smith & Nephew; Bristol-Myers Squibb Company; Cells for Cells; Stemedica Cell Technologies, Inc; and Castle Creek Biosciences, Inc. are among the companies operating in the cell therapy market.

Reasons to Buy

Key Topics Covered:

1. Introduction 1.1 Scope of the Study 1.2 Research Report Guidance 1.3 Market Segmentation 1.3.1 Global Cell Therapy Market - By Therapy Type 1.3.2 Global Cell Therapy Market - By Product 1.3.3 Global Cell Therapy Market - By Technology 1.3.4 Global Cell Therapy Market - By Application 1.3.5 Global Cell Therapy Market - By End User 1.3.6 Global Cell Therapy Market - By Geography

2. Cell Therapy Market - Key Takeaways

3. Research Methodology 3.1 Coverage 3.2 Secondary Research 3.3 Primary Research

4. Global Cell therapy- Market Landscape 4.1 Overview 4.2 PEST Analysis 4.2.1 North America - PEST Analysis 4.2.2 Europe- PEST Analysis 4.2.3 Asia Pacific- PEST Analysis 4.2.4 Middle East and Africa - PEST Analysis 4.2.5 South and Central America - PEST Analysis 4.3 Expert Opinions

5. Global Cell Therapy Market - Key Industry Dynamics 5.1 Key Market Drivers 5.1.1 Increasing Prevalence of Chronic Diseases 5.1.2 Rising Adoption of Regenerative Medicines 5.1.3 Increasing Number of Approvals for Cell-Based Therapies 5.2 Key Market Restraints 5.2.1 High Cost of Cell Therapy Manufacturing 5.3 Key Market Opportunities 5.3.1 Increasing Adoption of Cell Therapy in Developing Regions 5.4 Future Trends 5.4.1 Shift Toward Automated Cell Therapy Manufacturing 5.5 Impact Analysis of Drivers and Restraints

6. Cell therapy Market - Global Analysis 6.1 Global Cell therapy Market Revenue Forecast And Analysis 6.2 Global Cell therapy Market, By Geography - Forecast And Analysis 6.3 Market Positioning

7. Cell therapy Market Analysis - By Therapy Type 7.1 Overview 7.2 Cell therapy Market Revenue Share, by Therapy Type (2019 and 2027) 7.3 Allogeneic 7.3.1 Overview 7.3.2 Allogeneic: Cell therapy Market - Revenue and Forecast to 2027 (US$ Million) 7.4 Autologous 7.4.1 Overview 7.4.2 Autologous: Cell therapy Market - Revenue and Forecast to 2027 (US$ Million)

8. Cell therapy Market Analysis - By Product 8.1 Overview 8.2 Cell therapy Market Revenue Share, by Product (2019 and 2027) 8.3 Consumables 8.3.1 Overview 8.3.2 Consumables: Cell therapy Market - Revenue and Forecast to 2027 (US$ Million) 8.4 Equipment 8.4.1 Overview 8.4.2 Equipment: Cell therapy Market - Revenue and Forecast to 2027 (US$ Million) 8.5 Systems and Software 8.5.1 Overview 8.5.2 Systems and Software: Cell therapy Market - Revenue and Forecast to 2027 (US$ Million)

9. Cell therapy Market Analysis - By Technology 9.1 Overview 9.2 Cell therapy Market Revenue Share, by Technology (2019 and 2027) 9.3 Viral Vector Technology 9.3.1 Overview 9.3.2 Viral Vector Technology: Cell therapy Market - Revenue and Forecast to 2027 (US$ Million) 9.4 Genome Editing Technology 9.4.1 Overview 9.4.2 Genome Editing Technology: Cell therapy Market - Revenue and Forecast to 2027 (US$ Million) 9.5 Somatic Cell Technology 9.5.1 Overview 9.5.2 Somatic Cell Technology: Cell therapy Market - Revenue and Forecast to 2027 (US$ Million) 9.6 Cell Immortalization Technology 9.6.1 Overview 9.6.2 Cell Immortalization Technology: Cell therapy Market - Revenue and Forecast to 2027 (US$ Million) 9.7 Cell Plasticity Technology 9.7.1 Overview 9.7.2 Cell Plasticity Technology: Cell therapy Market - Revenue and Forecast to 2027 (US$ Million) 9.8 Three-Dimensional Technology 9.8.1 Overview 9.8.2 Three-Dimensional Technology: Cell therapy Market - Revenue and Forecast to 2027 (US$ Million)

10. Cell therapy Market Analysis - By Application 10.1 Overview 10.2 Cell therapy Market Revenue Share, by Application (2019 and 2027) 10.3 Oncology 10.3.1 Overview 10.3.2 Oncology: Cell therapy Market - Revenue and Forecast to 2027 (US$ Million) 10.4 Cardiovascular 10.4.1 Overview 10.4.2 Cardiovascular: Cell therapy Market - Revenue and Forecast to 2027 (US$ Million) 10.5 Orthopedic 10.5.1 Overview 10.5.2 Orthopedic: Cell therapy Market - Revenue and Forecast to 2027 (US$ Million) 10.6 Wound Management 10.6.1 Overview 10.6.2 Wound Management: Cell therapy Market - Revenue and Forecast to 2027 (US$ Million) 10.7 Other Applications 10.7.1 Overview 10.7.2 Other Applications: Cell therapy Market - Revenue and Forecast to 2027 (US$ Million)

11. Cell therapy Market Analysis - By End User 11.1 Overview 11.2 Cell therapy Market Share, by End User, 2019 and 2027, (%) 11.3 Hospitals 11.3.1 Overview 11.3.2 Hospitals: Cell therapy Market - Revenue and Forecast to 2027 (US$ Million) 11.4 Research Institutes 11.4.1 Overview 11.4.2 Research Institutes: Cell therapy Market - Revenue and Forecast to 2027 (US$ Million) 11.5 Others 11.5.1 Overview 11.5.2 Others: Cell therapy Market - Revenue and Forecast to 2027 (US$ Million)

12. Cell therapy Market - Geographic Analysis 12.1 North America: Cell Therapy Market 12.2 Europe: Cell therapy Market 12.3 Asia Pacific: Cell Therapy Market 12.4 Middle East and Africa: Cell Therapy Market 12.5 South and Central America: Cell Therapy Market

13. Impact of COVID-19 Pandemic on Global Cell Therapy Market 13.1 North America: Impact Assessment of COVID-19 Pandemic 13.2 Europe: Impact Assessment of COVID-19 Pandemic 13.3 Asia-Pacific: Impact Assessment of COVID-19 Pandemic 13.4 Middle East & Africa: Impact Assessment of COVID-19 Pandemic 13.5 South & Central America: Impact Assessment of COVID-19 Pandemic

14. Cell Therapy Market- Industry Landscape 14.1 Overview 14.2 Growth Strategies Done by the Companies in the Market, (%) 14.3 Organic Developments 14.3.1 Overview 14.4 Inorganic Developments 14.4.1 Overview

15. Company Profiles 15.1 Vericel Corporation 15.1.1 Key Facts 15.1.2 Business Description 15.1.3 Products and Services 15.1.4 Financial Overview 15.1.5 SWOT Analysis 15.1.6 Key Developments 15.2 MEDIPOST 15.2.1 Key Facts 15.2.2 Business Description 15.2.3 Products and Services 15.2.4 Financial Overview 15.2.5 SWOT Analysis 15.2.6 Key Developments 15.3 NuVasive, Inc. 15.3.1 Key Facts 15.3.2 Business Description 15.3.3 Products and Services 15.3.4 Financial Overview 15.3.5 SWOT Analysis 15.3.6 Key Developments 15.4 Mesoblast Limited 15.4.1 Key Facts 15.4.2 Business Description 15.4.3 Products and Services 15.4.4 Financial Overview 15.4.5 SWOT Analysis 15.4.6 Key Developments 15.5 JCR Pharmaceuticals Co. Ltd. 15.5.1 Key Facts 15.5.2 Business Description 15.5.3 Products and Services 15.5.4 Financial Overview 15.5.5 SWOT Analysis 15.5.6 Key Developments 15.6 Smith & Nephew 15.6.1 Key Facts 15.6.2 Business Description 15.6.3 Products and Services 15.6.4 Financial Overview 15.6.5 SWOT Analysis 15.6.6 Key Developments 15.7 Bristol-Myers Squibb Company 15.7.1 Key Facts 15.7.2 Business Description 15.7.3 Products and Services 15.7.4 Financial Overview 15.7.5 SWOT Analysis 15.7.6 Key Developments 15.8 Cells for Cells 15.8.1 Key Facts 15.8.2 Business Description 15.8.3 Products and Services 15.8.4 Financial Overview 15.8.5 SWOT Analysis 15.8.6 Key Developments 15.9 Stemedica Cell Technologies, Inc 15.9.1 Key Facts 15.9.2 Business Description 15.9.3 Products and Services 15.9.4 Financial Overview 15.9.5 SWOT Analysis 15.9.6 Key Developments 15.10 Castle Creek Biosciences, Inc. 15.10.1 Key Facts 15.10.2 Business Description 15.10.3 Products and Services 15.10.4 Financial Overview 15.10.5 SWOT Analysis 15.10.6 Key Developments

16. Appendix 16.1 About the Publisher 16.2 Glossary of Terms

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Outlook on the Cell Therapy Global Market to 2027 - by Therapy Type, Product, Technology, Application, End-user and Geography - GlobeNewswire