Rxivist combines preprints from bioRxiv with data from Twitter to help you find the papers being discussed in your field. Currently indexing 84,782 bioRxiv papers from 364,778 authors.
Most downloaded bioRxiv papers, all time
in category systems biology
2,196 results found. For more information, click each entry to expand.
2,984 downloads systems biology
Omics data contains signal from the molecular, physical, and kinetic inter- and intra-cellular interactions that control biological systems. Matrix factorization techniques can reveal low-dimensional structure from high-dimensional data that reflect these interactions. These techniques can uncover new biological knowledge from diverse high-throughput omics data in topics ranging from pathway discovery to time course analysis. We review exemplary applications of matrix factorization for systems-level analyses. We discuss appropriate application of these methods, their limitations, and focus on analysis of results to facilitate optimal biological interpretation. The inference of biologically relevant features with matrix factorization enables discovery from high-throughput data beyond the limits of current biological knowledge-answering questions from high-dimensional data that we have not yet thought to ask.
2,953 downloads systems biology
Technologies that visualize multiple biomolecules at the nanometer scale in cells will enable deeper understanding of biological processes that proceed at the molecular scale. Current fluorescence-based methods for microscopy are constrained by a combination of spatial resolution limitations, limited parameters per experiment, and detector systems for the wide variety of biomolecules found in cells. We present here super-resolution ion beam imaging (srIBI), a secondary ion mass spectrometry approach capable of high-parameter imaging in 3D of targeted biological entities and exogenously added small molecules. Uniquely, the atomic constituents of the biomolecules themselves can often be used in our system as the "tag". We visualized the subcellular localization of the chemotherapy drug cisplatin simultaneously with localization of five other nuclear structures, with further carbon elemental mapping and secondary electron visualization, down to ~30 nm lateral resolution. Cisplatin was preferentially enriched in nuclear speckles and excluded from closed-chromatin regions, indicative of a role for cisplatin in active regions of chromatin. These data highlight how multiplexed super-resolution techniques, such as srIBI, will enable studies of biomolecule distributions in biologically relevant subcellular microenvironments.
2,929 downloads systems biology
Integrated -omics approaches are quickly spreading across microbiology research labs, leading to i) the possibility of detecting previously hidden features of microbial cells like multi-scale spatial organisation and ii) tracing molecular components across multiple cellular functional states. This promises to reduce the knowledge gap between genotype and phenotype and poses new challenges for computational microbiologists. We underline how the capability to unravel the complexity of microbial life will strongly depend on the integration of the huge and diverse amount of information that can be derived today from -omics experiments. In this work, we present opportunities and challenges of multi –omics data integration in current systems biology pipelines. We here discuss which layers of biological information are important for biotechnological and clinical purposes, with a special focus on bacterial metabolism and modelling procedures. A general review of the most recent computational tools for performing large-scale datasets integration is also presented, together with a possible framework to guide the design of systems biology experiments by microbiologists.
2,918 downloads systems biology
The observations of phenotypic plasticity have stimulated the revival of 'epigenetics'. Over the past 70 years the term has come in many colors and flavors, depending on the biological discipline and time period. The meanings span from Waddington's "epigenotype" and "epigenetic landscape" to the molecular biologists' "epigenetic marks" embodied by DNA methylation and histone modifications. Here we seek to quell the ambiguity of the name. First we place "epigenetic" in the various historical contexts. Then, by presenting the formal concepts of dynamical systems theory we show that the "epigenetic landscape" is more than a metaphor: it has specific mathematical foundations. The latter explains how gene regulatory networks produce multiple attractor states, the self-stabilizing patterns of gene activation across the genome that account for "epigenetic memory". This network dynamics approach replaces the reductionist correspondence of molecular epigenetic modifications with concept of the epigenetic landscape, by providing a concrete and crisp correspondence.
2,893 downloads systems biology
Nucleosomes restrict DNA accessibility throughout eukaryotic genomes, with repercussions for replication, transcription, and other DNA-templated processes. How this globally restrictive organization emerged from a presumably more open ancestral state remains poorly understood. Here, to better understand the challenges associated with establishing globally restrictive chromatin, we express histones in a naïve bacterial system that has not evolved to deal with nucleosomal structures: Escherichia coli . We find that histone proteins from the archaeon Methanothermus fervidus assemble on the E. coli chromosome in vivo and protect DNA from micrococcal nuclease digestion, allowing us to map binding footprints genome-wide. We provide evidence that nucleosome occupancy along the E. coli genome tracks intrinsic sequence preferences but is disturbed by ongoing transcription and replication. Notably, we show that higher nucleosome occupancy at promoters and across gene bodies is associated with lower transcript levels, consistent with local repressive effects. Surprisingly, however, this sudden enforced chromatinization has only mild repercussions for growth, suggesting that histones can become established as ubiquitous chromatin proteins without interfering critically with key DNA-templated processes. Our results have implications for the evolvability of transcriptional ground states and highlight chromatinization by archaeal histones as a potential avenue for controlling genome accessibility in synthetic prokaryotic systems.
2,873 downloads systems biology
Motivation: Parameter estimation methods for ordinary differential equation (ODE) models of biological processes can exploit gradients and Hessians of objective functions to achieve convergence and computational efficiency. However, the computational complexity of established methods to evaluate the Hessian scales linearly with the number of state variables and quadratically with the number of parameters. This limits their application to low-dimensional problems. Results: We introduce second order adjoint sensitivity analysis for the computation of Hessians and a hybrid optimization-integration based approach for profile likelihood computation. Second order adjoint sensitivity analysis scales linearly with the number of parameters and state variables. The Hessians are effectively exploited by the proposed profile likelihood computation approach. We evaluate our approaches on published biological models with real measurement data. Our study reveals an improved computational efficiency and robustness of optimization compared to established approaches, when using Hessians computed with adjoint sensitivity analysis. The hybrid computation method was more than two-fold faster than the best competitor. Thus, the proposed methods and implemented algorithms allow for the improvement of parameter estimation for medium and large scale ODE models. Availability: The algorithms for second order adjoint sensitivity analysis are implemented in the Advance MATLAB Interface CVODES and IDAS (AMICI, https://github.com/ICB-DCM/AMICI/). The algorithm for hybrid profile likelihood computation is implemented in the parameter estimation toolbox (PESTO, https://github.com/ICB-DCM/PESTO/). Both toolboxes are freely available under the BSD license.
2,851 downloads systems biology
Florian Meier, Andreas-David Brunner, Max Frank, Annie Ha, Isabell Bludau, Eugenia Voytik, Stephanie Kaspar-Schoenefeld, Markus Lubeck, Oliver Raether, Ruedi Aebersold, Ben C. Collins, Hannes Röst, Matthias Mann
Data independent acquisition (DIA) modes isolate and concurrently fragment populations of different precursors by cycling through segments of a predefined precursor m/z range. Although these selection windows collectively cover the entire m/z range, overall only a few percent of all incoming ions are sampled. Making use of the correlation of molecular weight and ion mobility in a trapped ion mobility device (timsTOF Pro), we here devise a novel scan mode that samples up to 100% of the peptide precursor ion current. We extend an established targeted data extraction workflow by including the ion mobility dimension for both signal extraction and scoring, thereby increasing the specificity for precursor identification. Data acquired from whole proteome digests and mixed organism samples demonstrate deep proteome coverage and a very high degree of reproducibility as well as quantitative accuracy, even from 10 ng sample amounts.
2,833 downloads systems biology
Tapio Lönnberg, Valentine Svensson, Kylie R James, Daniel Fernandez-Ruiz, Ismail Sebina, Ruddy Montandon, Megan S F Soon, Lily G Fogg, Michael J. T. Stubbington, Frederik Otzen Bagger, Max Zwiessele, Neil Lawrence, Fernando Souza-Fonseca-Guimaraes, William R Heath, Oliver Billker, Oliver Stegle, Ashraful Haque, Sarah A Teichmann
Differentiation of naïve CD4+ T cells into functionally distinct T helper subsets is crucial for the orchestration of immune responses. Due to multiple levels of heterogeneity and multiple overlapping transcriptional programs in differentiating T cell populations, this process has remained a challenge for systematic dissection in vivo. By using single-cell RNA transcriptomics and computational modelling of temporal mixtures, we reconstructed the developmental trajectories of Th1 and Tfh cell populations during Plasmodium infection in mice at single-cell resolution. These cell fates emerged from a common, highly proliferative and metabolically active precursor. Moreover, by tracking clonality from T cell receptor sequences, we infer that ancestors derived from the same naïve CD4+ T cell can concurrently populate both Th1 and Tfh subsets. We further found that precursor T cells were coached towards a Th1 but not a Tfh fate by monocytes/macrophages. The integrated genomic and computational approach we describe is applicable for analysis of any cellular system characterized by differentiation towards multiple fates.
2,761 downloads systems biology
Mass spectrometry is the method of choice for deep and comprehensive analysis of proteomes and has become a key technology to support the progress in life science and biomedicine. However, sample preparation in proteomics is not standardized and contributes to a lack of reproducibility. The main challenge is to extract all proteins in a manner that enables efficient digestion into peptides and is compatible with subsequent mass spectrometric analysis. Current methods are based on the idea of removing detergents or chaotropic agents during sample processing, which are essential for protein extraction but interfere with digestion and LC-MS. These multi-step preparations are prone to losses, biases and contaminations, while being time-consuming and labor-intensive. We report a universal detergent-free method, named Sample Preparation by Easy Extraction and Digestion (SPEED), which is based on a simple three-step procedure, acidification, neutralization and digestion. SPEED is a one-pot method for peptide generation from various sources and is easily applicable even for lysis-resistant sample types as pure trifluoroacetic acid (TFA) is used for highly efficient protein extraction. SPEED-based sample processing is highly reproducible, provides exceptional peptide yields and enables preparation even of tissue samples with less than 15 min hands-on time and without any special equipment. Evaluation of SPEED performance revealed, that the number of quantified proteins and the quantitative reproducibility are superior compared to the well-established sample processing protocols FASP, ISD-Urea and SP3 for various sample types, including human cells, bacteria and tissue, even at low protein starting amounts.
2,739 downloads systems biology
The intestinal epithelium is a highly structured tissue composed of repeating crypt-villus units. Enterocytes, which constitute the most abundant cell type, perform the diverse tasks of absorbing a wide range of nutrients while protecting the body from the harsh bacterial-rich environment. It is unknown if these tasks are equally performed by all enterocytes or whether they are spatially zonated along the villus axis. Here, we performed whole-transcriptome measurements of laser-capture-microdissected villus segments to extract a large panel of landmark genes, expressed in a zonated manner. We used these genes to localize single sequenced enterocytes along the villus axis, thus reconstructing a global spatial expression map. We found that most enterocyte genes were zonated. Enterocytes at villi bottoms expressed an anti-bacterial Reg gene program in a microbiome-dependent manner, potentially reducing the crypt pathogen exposure. Translation, splicing and respiration genes steadily decreased in expression towards the villi tops, whereas distinct mid-top villus zones sub-specialized in the absorption of carbohydrates, peptides and fat. Enterocytes at the villi tips exhibited a unique gene-expression signature consisting of Klf4, Egfr, Neat1, Malat1, cell adhesion and purine metabolism genes. Our study exposes broad spatial heterogeneity of enterocytes, which could be important for achieving their diverse tasks.
2,685 downloads systems biology
Background: Numerous centrality measures have been introduced to identify "central" nodes in large networks. The availability of a wide range of measures for ranking influential nodes leaves the user to decide which measure may best suit the analysis of a given network. The choice of a suitable measure is furthermore complicated by the impact of the network topology on ranking influential nodes by centrality measures. To approach this problem systematically, we examined the centrality profile of nodes of yeast protein-protein interaction networks (PPINs) in order to detect which centrality measure is succeeding in predicting influential proteins. We studied how different topological network features are reflected in a large set of commonly used centrality measures. Results: We used yeast PPINs to compare 27 common of centrality measures. The measures characterize and assort influential nodes of the networks. We applied principal component analysis (PCA) and hierarchical clustering and found that the most informative measures depend on the network's topology. Interestingly, some measures had a high level of contribution in comparison to others in all PPINs, namely Latora closeness, Decay, Lin, Freeman closeness, Diffusion, Residual closeness and Average distance centralities. Conclusions: The choice of a suitable set of centrality measures is crucial for inferring important functional properties of a network. We concluded that undertaking data reduction using unsupervised machine learning methods helps to choose appropriate variables (centrality measures). Hence, we proposed identifying the contribution proportions of the centrality measures with PCA as a prerequisite step of network analysis before inferring functional consequences, e.g., essentiality of a node.
2,659 downloads systems biology
Florian Meier, Andreas-David Brunner, Scarlet Koch, Heiner Koch, Markus Lubeck, Michael Krause, Niels Goedecke, Jens Decker, Thomas Kosinski, Melvin A Park, Nicolai Bache, Ole Hoerning, Jüergen Cox, Oliver Räther, Matthias Mann
In bottom−up proteomics, peptides are separated by liquid chromatography with elution peak widths in the range of seconds, while mass spectra are acquired in about 100 microseconds with time−of−fight (TOF) instruments. This allows adding ion mobility as a third dimension of separation. Among several formats, trapped ion mobility spectrometry (TIMS) is attractive due to its small size, low voltage requirements and high efficiency of ion utilization. We have recently demonstrated a scan mode termed parallel accumulation − serial fragmentation (PASEF), which multiplies the sequencing speed without any loss in sensitivity (Meier et al., PMID: 26538118). Here we introduce the timsTOF Pro instrument, which optimally implements online PASEF. It features an orthogonal ion path into the ion mobility device, limiting the amount of debris entering the instrument and making it very robust in daily operation. We investigate different precursor selection schemes for shotgun proteomics to optimally allocate in excess of 100 fragmentation events per second. More than 800,000 fragmentation spectra in standard 120 min LC runs are easily achievable, which can be used for near exhaustive precursor selection in complex mixtures or re-sequencing weak precursors. MaxQuant identified more than 6,400 proteins in single run HeLa analyses without matching to a library, and with high quantitative reproducibility (R > 0.97). Online PASEF achieves a remarkable sensitivity with more than 2,900 proteins identified in 30 min runs of only 10 ng HeLa digest. We also show that highly reproducible collisional cross sections can be acquired on a large scale (R > 0.99). PASEF on the timsTOF Pro is a valuable addition to the technological toolbox in proteomics, with a number of unique operating modes that are only beginning to be explored.
2,616 downloads systems biology
Nucleosomes cover most of the genome and are thought to be displaced by transcription factors (TFs) in regions that direct gene expression. However, the modes of interaction between TFs and nucleosomal DNA remain largely unknown. Here, we use nucleosome consecutive affinity-purification systematic evolution of ligands by exponential enrichment (NCAP-SELEX) to systematically explore interactions between the nucleosome and 220 TFs representing diverse structural families. Consistently with earlier observations, we find that the vast majority of TFs have less access to nucleosomal DNA than to free DNA. The motifs recovered from TFs bound to nucleosomal and free DNA are generally similar; however, steric hindrance and scaffolding by the nucleosome result in specific positioning and orientation of the motifs. Many TFs preferentially bind close to the end of nucleosomal DNA, or to periodic positions at its solvent-exposed side. TFs often also bind nucleosomal DNA in a particular orientation, because the nucleosome breaks the local rotational symmetry of DNA. Some TFs also specifically interact with DNA located at the dyad position where only one DNA gyre is wound, whereas other TFs prefer sites spanning two DNA gyres and bind specifically to each of them. Our work reveals striking differences in TF binding to free and nucleosomal DNA, and uncovers a rich interaction landscape between the TFs and the nucleosome.
2,608 downloads systems biology
Wyler Emanuel, Mösbauer Kirstin, Franke Vedran, Diag Asija, Gottula Lina Theresa, Arsie Roberto, Klironomos Filippos, Koppstein David, Ayoub Salah, Buccitelli Christopher, Richter Anja, Legnini Ivano, Ivanov Andranik, Mari Tommaso, Del Giudice Simone, Papies Jan Patrick, Müller Marcel Alexander, Niemeyer Daniela, Selbach Matthias, Akalin Altuna, Rajewsky Nikolaus, Drosten Christian, Landthaler Markus
The coronavirus disease 2019 (COVID-19) pandemic, caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is an ongoing global health threat with more than two million infected people since its emergence in late 2019. Detailed knowledge of the molecular biology of the infection is indispensable for understanding of the viral replication, host responses, and disease progression. We provide gene expression profiles of SARS-CoV and SARS-CoV-2 infections in three human cell lines (H1299, Caco-2 and Calu-3 cells), using bulk and single-cell transcriptomics. Small RNA profiling showed strong expression of the immunity and inflammation-associated microRNA miRNA-155 upon infection with both viruses. SARS-CoV-2 elicited approximately two-fold higher stimulation of the interferon response compared to SARS-CoV in the permissive human epithelial cell line Calu-3, and induction of cytokines such as CXCL10 or IL6. Single cell RNA sequencing data showed that canonical interferon stimulated genes such as IFIT2 or OAS2 were broadly induced, whereas interferon beta (IFNB1) and lambda (IFNL1-4) were expressed only in a subset of infected cells. In addition, temporal resolution of transcriptional responses suggested interferon regulatory factors (IRFs) activities precede that of nuclear factor-κB (NF-κB). Lastly, we identified heat shock protein 90 (HSP90) as a protein relevant for the infection. Inhibition of the HSP90 charperone activity by Tanespimycin/17-N-allylamino-17-demethoxygeldanamycin (17-AAG) resulted in a reduction of viral replication, and of TNF and IL1B mRNA levels. In summary, our study established in vitro cell culture models to study SARS-CoV-2 infection and identified HSP90 protein as potential drug target for therapeutic intervention of SARS-CoV-2 infection. ### Competing Interest Statement The authors have declared no competing interest.
2,584 downloads systems biology
Single-cell time-lapse studies have advanced the quantitative understanding of cell-to-cell variability. However, as the information content of individual experiments is limited, methods to integrate data collected under different conditions are required. Here we present a multi-experiment nonlinear mixed effect modeling approach for mechanistic pathway models, which allows the integration of multiple single-cell perturbation experiments. We apply this approach to the translation of green fluorescent protein after transfection using a massively parallel read-out of micropatterned single-cell arrays. We demonstrate that the integration of data from perturbation experiments allows the robust reconstruction of cell-to-cell variability, i.e., parameter densities, while each individual experiment provides insufficient information. Indeed, we show that the integration of the datasets on the population level also improves the estimates for individual cells by breaking symmetries, although each of them is only measured in one experiment. Moreover, we confirmed that the suggested approach is robust with respect to batch effects across experimental replicates and can provide mechanistic insights into the nature of batch effects. We anticipate that the proposed multi-experiment nonlinear mixed effect modeling approach will serve as a basis for the analysis of cellular heterogeneity in single-cell dynamics.
2,571 downloads systems biology
Molecular differences between individual cells can lead to dramatic differences in cell fate, such as the difference between death versus survival of cancer cells upon treatment with anti-cancer drugs. These originating differences have remained hidden, however, due to our inability to precisely determine what variable molecular features lead to what cellular fates. Here, we trace drug-resistant cell fates back to differences in the molecular profiles of their drug-naive melanoma precursors, revealing a rich substructure of variability underlying a number of resistant phenotypes at the single cell level. We make these connections using Rewind, a methodology that combines genetic barcoding with an RNA-based readout to directly capture rare cells that give rise to cellular behaviors of interest. We performed extensive single cell analysis to identify differences in gene expression and MAP-kinase signaling that mark a rare population of drug-naive cells (initial frequency of ~1:1000-1:10,000 cells) that ultimately gives rise to drug resistant clones. We demonstrate that this rare subpopulation has rich substructure and is composed of several distinct subpopulations, and the molecular differences between these subpopulations predict future differences in phenotypic behavior, such as the ultimate proliferative capacity of drug resistant cells. Similarly, we show that treatments that modify the frequency of resistance can allow otherwise non-resistant cells in the drug-naive population to become resistant, and that these new populations are marked by the variable expression of distinct genes. Together, our results reveal the presence of hidden, rare-cell variability that can underlie a range of latent phenotypic outcomes upon drug exposure. ### Competing Interest Statement AR receives consulting income and AR and SMS receive royalties related to Stellaris RNA FISH probes.
2,567 downloads systems biology
We hypothesized that transcription factors (TFs) recognize DNA shape without nucleotide sequence recognition. Motivating an independent role for shape, many TF binding sites lack a sequence-motif, DNA shape adds specificity to sequence-motifs, and different sequences can encode similar shapes. We therefore asked if binding sites of a TF are enriched for specific patterns of DNA shape-features, e.g., helical twist. We developed ShapeMF, which discovers these shape-motifs de novo without taking sequence information into account. We find that most TFs assayed in ENCODE have shape-motifs and bind regulatory regions recognizing shape-motifs in the absence of sequence-motifs. When shape- and sequence-recognition co-occur, the two types of motifs can be overlapping, flanking, or separated by consistent spacing. Shape-motifs are prevalent in regions co-bound by multiple TFs. Finally, TFs with identical sequence motifs have different shape-motifs, explaining their binding at distinct locations. These results establish shape-motifs as drivers of TF-DNA recognition complementary to sequence-motifs.
2,566 downloads systems biology
Ilias Angelidis, Lukas M. Simon, Isis E Fernandez, Maximilian Strunz, Christoph H Mayr, Flavia R Greiffo, George Tsitsiridis, Elisabeth Graf, Tim-Matthias Strom, Oliver Eickelberg, Matthias Mann, Fabian J. Theis, Herbert B Schiller
Aging promotes lung function decline and susceptibility to chronic lung diseases, which are the third leading cause of death worldwide. We used single cell transcriptomics and mass spectrometry to quantify changes in cellular activity states of 30 cell types and the tissue proteome from lungs of young and old mice. Aging led to increased transcriptional noise, indicating deregulated epigenetic control. We observed highly distinct effects of aging on cell type level, uncovering increased cholesterol biosynthesis in type-2 pneumocytes and lipofibroblasts as a novel hallmark of lung aging. Proteomic profiling revealed extracellular matrix remodeling in old mice, including increased collagen IV and XVI and decreased Fraser syndrome complex proteins and Collagen XIV. Computational integration of the aging proteome and single cell transcriptomes predicted the cellular source of regulated proteins and created a first unbiased reference of the aging lung. The lung aging atlas can be accessed via an interactive user-friendly webtool at: https://theislab.github.io/LungAgingAtlas
2,563 downloads systems biology
Olivia Wilkins, Christoph Hafemeister, Anne Plessis, Meisha-Marika Holloway-Phillips, Gina M. Pham, Adrienne B Nicotra, Glenn B. Gregorio, S.V. Krishna Jagadish, Endang M. Septiningsih, Richard Bonneau, Michael Purugganan
Environmental Gene Regulatory Influence Networks (EGRINs) coordinate the timing and rate of gene expression in response to environmental and developmental signals. EGRINs encompass many layers of regulation, which culminate in changes in the level of accumulated transcripts. Here we infer EGRINs for the response of five tropical Asian rice cultivars to high temperatures, water deficit, and agricultural field conditions, by systematically integrating time series transcriptome data (720 RNA-seq libraries), patterns of nucleosome-free chromatin (18 ATAC-seq libraries), and the occurrence of known cis-regulatory elements. First, we identify 5,447 putative target genes for 445 transcription factors (TFs) by connecting TFs with genes with known cis-regulatory motifs in nucleosome-free chromatin regions proximal to transcriptional start sites (TSS) of genes. We then use network component analysis to estimate the regulatory activity for these TFs from the expression of these putative target genes. Finally, we inferred an EGRIN using the estimated TFA as the regulator. The EGRIN included regulatory interactions between 4,052 target genes regulated by 113 TFs. We resolved distinct regulatory roles for members of a large TF family, including a putative regulatory connection between abiotic stress and the circadian clock, as well as specific regulatory functions for TFs in the drought response. TFA estimation using network component analysis is an effective way of incorporating multiple genome-scale measurements into network inference and that supplementing data from controlled experimental conditions with data from outdoor field conditions increases the resolution for EGRIN inference.
2,556 downloads systems biology
Samuel W. Lukowski, Camden Y. Lo, Alexei Sharov, Quan H. Nguyen, Lyujie Fang, Sandy SC Hung, Ling Zhu, Ting Zhang, Tu Nguyen, Anne Senabouth, Jafar S Jabbari, Emily Welby, Jane C. Sowden, Hayley S. Waugh, Adrienne Mackey, Graeme Pollock, Trevor D. Lamb, Peng-Yuan Wang, Alex W. Hewitt, Mark Gillies, Joseph E. Powell, Raymond CB Wong
The retina is a highly specialized neural tissue that senses light and initiates image processing. Although the functional organisation of specific cells within the retina has been well-studied, the molecular profile of many cell types remains unclear in humans. To comprehensively profile cell types in the human retina, we performed single cell RNA-sequencing on 20,009 cells obtained post-mortem from three donors and compiled a reference transcriptome atlas. Using unsupervised clustering analysis, we identified 18 transcriptionally distinct clusters representing all known retinal cells: rod photoreceptors, cone photoreceptors, Müller glia cells, bipolar cells, amacrine cells, retinal ganglion cells, horizontal cells, retinal astrocytes and microglia. Notably, our data captured molecular profiles for healthy and early degenerating rod photoreceptors, and revealed a novel role of MALAT1 in putative rod degeneration. We also demonstrated the use of this retina transcriptome atlas to benchmark pluripotent stem cell-derived cone photoreceptors and an adult Müller glia cell line. This work provides an important reference with unprecedented insights into the transcriptional landscape of human retinal cells, which is fundamental to our understanding of retinal biology and disease.
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