in silico transcriptome dissection of neocortical excitatory neurogenesis via joint matrix decomposition and transfer learning.

The rising quality and amount of multi-omic data across biomedical science demands that we build innovative solutions to harness their collective discovery potential. From publicly available repositories, we have assembled and curated a compendium of gene-level transcriptomic data focused on mammalian excitatory neurogenesis in the neocortex. This collection is open for exploration by both computational and cell biologists at nemoanalytics.org, and this report forms a demonstration of its utility. Applying our novel structured joint decomposition approach to mouse, macaque and human data from the collection, we define transcriptome dynamics that are conserved across mammalian excitatory neurogenesis and which map onto the genetics of human brain structure and disease. Leveraging additional data within NeMO Analytics via projection methods, we chart the dynamics of these fundamental molecular elements of neurogenesis across developmental time and space and into postnatal life. Reversing the direction of our investigation, we use transcriptomic data from laminar-specific dissection of adult human neocortex to define molecular signatures specific to excitatory neuronal cell types resident in individual layers of the mature neocortex, and trace their emergence across development. We show that while many lineage defining transcription factors are most highly expressed at early fetal ages, the laminar neuronal identities which they drive take years to decades to reach full maturity. Finally, we interrogated data from stem-cell derived cerebral organoid systems demonstrating that many fundamental elements of in vivo development are recapitulated with high-fidelity in vitro, while specific transcriptomic programs in neuronal maturation are absent. We propose these analyses as specific applications of the general approach of combining joint decomposition with large curated collections of analysis-ready multi-omics data matrices focused on particular cell and disease contexts. Importantly, these open environments are accessible to, and must be fueled with emerging data by, cell biologists with and without coding expertise.

Stress-induced ß cell early senescence confers protection against type 1 diabetes.

During the progression of type 1 diabetes (T1D), ß cells are exposed to significant stress and, therefore, require adaptive responses to survive. The adaptive mechanisms that can preserve ß cell function and survival in the face of autoimmunity remain unclear. Here, we show that the deletion of the unfolded protein response (UPR) genes Atf6α or Ire1α in ß cells of non-obese diabetic (NOD) mice prior to insulitis generates a p21-driven early senescence phenotype and alters the ß cell secretome that significantly enhances the leukemia inhibitory factor-mediated recruitment of M2 macrophages to islets. Consequently, M2 macrophages promote anti-inflammatory responses and immune surveillance that cause the resolution of islet inflammation, the removal of terminally senesced ß cells, the reduction of ß cell apoptosis, and protection against T1D. We further demonstrate that the p21-mediated early senescence signature is conserved in the residual ß cells of T1D patients. Our findings reveal a previously unrecognized link between ß cell UPR and senescence that, if leveraged, may represent a novel preventive strategy for T1D.

CellCover Defines Conserved Cell Types and Temporal Progression in scRNA-seq Data across Mammalian Neocortical Development.

Accurate identification of cell classes across the tissues of living organisms is central in the analysis of growing atlases of single-cell RNA sequencing (scRNA-seq) data across biomedicine. Such analyses are often based on the existence of highly discriminating "marker genes" for specific cell classes which enables a deeper functional understanding of these classes as well as their identification in new, related datasets. Currently, marker genes are defined by methods that serially assess the level of differential expression (DE) of individual genes across landscapes of diverse cells. This serial approach has been extremely useful, but is limited because it ignores possible redundancy or complementarity across genes, that can only be captured by analyzing several genes at the same time. We wish to identify discriminating panels of genes. To efficiently explore the vast space of possible marker panels, leverage the large number of cells often sequenced, and overcome zero-inflation in scRNA-seq data, we propose viewing panel selection as a variation of the "minimal set-covering problem" in combinatorial optimization which can be solved with integer programming. In this formulation, the covering elements are genes, and the objects to be covered are cells of a particular class, where a cell is covered by a gene if that gene is expressed in that cell. Our method, CellCover, identifies a panel of marker genes in scRNA-seq data that covers one class of cells within a population. We apply this method to generate covering marker gene panels which characterize cells of the developing mouse neocortex as postmitotic neurons are generated from neural progenitor cells (NPCs). We show that CellCover captures cell class-specific signals distinct from those defined by DE methods and that CellCover's compact gene panels can be expanded to explore cell type specific function.Transfer learning experiments exploring these covering panels across in vivo mouse, primate, and human scRNA-seq datasets demonstrate that CellCover identifies markers of conserved cell classes in neurogenesis, as well as markers of temporal progression in the molecular identity of these cell types across development of the mammalian neocortex. The gene covering panels we identify across cell types and developmental time can be freely explored in visualizations across all the public data we use in this report at with NeMo Analytics [1] through https://nemoanalytics.org/p?l=CellCover . The code for CellCover is written in R and the Gurobi R interface and is available at [2].

The Neuroscience Multi-Omic Archive: a BRAIN Initiative resource for single-cell transcriptomic and epigenomic data from the mammalian brain.

Scalable technologies to sequence the transcriptomes and epigenomes of single cells are transforming our understanding of cell types and cell states. The Brain Research through Advancing Innovative Neurotechnologies (BRAIN) Initiative Cell Census Network (BICCN) is applying these technologies at unprecedented scale to map the cell types in the mammalian brain. In an effort to increase data FAIRness (Findable, Accessible, Interoperable, Reusable), the NIH has established repositories to make data generated by the BICCN and related BRAIN Initiative projects accessible to the broader research community. Here, we describe the Neuroscience Multi-Omic Archive (NeMO Archive; nemoarchive.org), which serves as the primary repository for genomics data from the BRAIN Initiative. Working closely with other BRAIN Initiative researchers, we have organized these data into a continually expanding, curated repository, which contains transcriptomic and epigenomic data from over 50 million brain cells, including single-cell genomic data from all of the major regions of the adult and prenatal human and mouse brains, as well as substantial single-cell genomic data from non-human primates. We make available several tools for accessing these data, including a searchable web portal, a cloud-computing interface for large-scale data processing (implemented on Terra, terra.bio), and a visualization and analysis platform, NeMO Analytics (nemoanalytics.org).

Beta Cell Dedifferentiation Induced by IRE1α Deletion Prevents Type 1 Diabetes.

Immune-mediated destruction of insulin-producing ß cells causes type 1 diabetes (T1D). However, how ß cells participate in their own destruction during the disease process is poorly understood. Here, we report that modulating the unfolded protein response (UPR) in ß cells of non-obese diabetic (NOD) mice by deleting the UPR sensor IRE1α prior to insulitis induced a transient dedifferentiation of ß cells, resulting in substantially reduced islet immune cell infiltration and ß cell apoptosis. Single-cell and whole-islet transcriptomics analyses of immature ß cells revealed remarkably diminished expression of ß cell autoantigens and MHC class I components, and upregulation of immune inhibitory markers. IRE1α-deficient mice exhibited significantly fewer cytotoxic CD8+ T cells in their pancreata, and adoptive transfer of their total T cells did not induce diabetes in Rag1-/- mice. Our results indicate that inducing ß cell dedifferentiation, prior to insulitis, allows these cells to escape immune-mediated destruction and may be used as a novel preventive strategy for T1D in high-risk individuals.