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The retina is a specialized neural tissue that senses light and initiates image processing. Although the functional organization of specific retina cells has been well studied, the molecular profile of many cell types remains unclear in humans. To comprehensively profile the human retina, we performed single‐cell RNA sequencing on 20,009 cells from three donors and compiled a reference transcriptome atlas. Using unsupervised clustering analysis, we identified 18 transcriptionally distinct cell populations representing all known neural retinal cells: rod photoreceptors, cone photoreceptors, Müller glia, bipolar cells, amacrine cells, retinal ganglion cells, horizontal cells, astrocytes, and microglia. Our data captured molecular profiles for healthy and putative early degenerating rod photoreceptors, and revealed the loss of MALAT1 expression with longer post‐mortem time, which potentially suggested a novel role of MALAT1 in rod photoreceptor degeneration. We have 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 understanding retinal biology and disease.

A modified Chromium 10x droplet-based protocol that subsamples cells for both short-read and long-read (nanopore) sequencing together with a new computational pipeline (FLAMES) is developed to enable isoform discovery, splicing analysis, and mutation detection in single cells. We identify thousands of unannotated isoforms and find conserved functional modules that are enriched for alternative transcript usage in different cell types and species, including ribosome biogenesis and mRNA splicing. Analysis at the transcript level allows data integration with scATAC-seq on individual promoters, improved correlation with protein expression data, and linked mutations known to confer drug resistance to transcriptome heterogeneity.

et al. describe 17 patients with recurrent de novo ATAD3 duplications resulting in stably expressed chimeric ATAD3A/ATAD3C proteins and altered ATAD3 oligomerization. Affected individuals share striking clinical similarities featuring cardiomyopathy, perinatal death, and cardiac complex I deficiency, with ATAD3 emerging as a hotspot for pathogenic genomic variation leading to mitochondrial disease.

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Background Single-cell RNA-sequencing (scRNA-seq) technologies and associated analysis methods have rapidly developed in recent years. This includes preprocessing methods, which assign sequencing reads to genes to create count matrices for downstream analysis. While several packaged preprocessing workflows have been developed to provide users with convenient tools for handling this process, how they compare to one another and how they influence downstream analysis have not been well studied. Results Here, we systematically benchmark the performance of 10 end-to-end preprocessing workflows (Cell Ranger, Optimus, salmon alevin, alevin-fry, kallisto bustools, dropSeqPipe, scPipe, zUMIs, celseq2, and scruff) using datasets yielding different biological complexity levels generated by CEL-Seq2 and 10x Chromium platforms. We compare these workflows in terms of their quantification properties directly and their impact on normalization and clustering by evaluating the performance of different method combinations. While the scRNA-seq preprocessing workflows compared vary in their detection and quantification of genes across datasets, after downstream analysis with performant normalization and clustering methods, almost all combinations produce clustering results that agree well with the known cell type labels that provided the ground truth in our analysis. Conclusions In summary, the choice of preprocessing method was found to be less important than other steps in the scRNA-seq analysis process. Our study comprehensively compares common scRNA-seq preprocessing workflows and summarizes their characteristics to guide workflow users.

Alternative splicing shapes the phenotype of cells in development and disease. Long-read RNA-sequencing recovers full-length transcripts but has limited throughput at the single-cell level. Here we developed single-cell full-length transcript sequencing by sampling (FLT-seq), together with the computational pipeline FLAMES to overcome these issues and perform isoform discovery and quantification, splicing analysis and mutation detection in single cells. With FLT-seq and FLAMES, we performed the first comprehensive characterization of the full-length isoform landscape in single cells of different types and species and identified thousands of unannotated isoforms. We found conserved functional modules that were enriched for alternative transcript usage in different cell populations, including ribosome biogenesis and mRNA splicing. Analysis at the transcript-level allowed data integration with scATAC-seq on individual promoters, improved correlation with protein expression data and linked mutations known to confer drug resistance to transcriptome heterogeneity. Our methods reveal previously unseen isoform complexity and provide a better framework for multi-omics data integration.

Venetoclax inhibits the pro-survival protein BCL2 to induce apoptosis and is a standard therapy for chronic lymphocytic leukemia (CLL), delivering high complete remission rates and prolonged progression-free survival in relapsed CLL, but with eventual loss of efficacy. A spectrum of sub-clonal genetic changes associated with venetoclax resistance have now been described. To fully understand clinical resistance to venetoclax, we combined single-cell short- and long‑read RNA‑sequencing to reveal the previously unappreciated scale of genetic and epigenetic changes underpinning acquired venetoclax resistance. These appear to be multi-layered. One layer comprises changes in the BCL2 family of apoptosis regulators, especially the pro-survival family members. This includes previously described mutations in BCL2 and amplification of the MCL1 gene but heterogeneous across and within individual patient's leukemias. Changes in the pro-apoptotic genes are notably uncommon, except for single cases with sub-clonal losses of BAX or NOXA. Much more prominent was universal MCL1 gene upregulation. This was driven by an overlying layer of emergent NF‑kB activation which persisted in circulating cells during venetoclax therapy. We discovered that MCL1 could be a direct transcriptional target of NF‑kB. Both the switch to alternative pro-survival factors and NF‑kB activation largely dissipate following venetoclax discontinuation. Our studies reveal the extent of plasticity of CLL cells in their ability to evade venetoclax-induced apoptosis. Importantly, these findings pinpoint new approaches to circumvent venetoclax resistance and provide a specific biological justification for the strategy of venetoclax discontinuation once maximal response is achieved rather than maintaining long-term selective pressure with the drug.