Integrated spatial genomics in tissues reveals invariant and cell type dependent nuclear architecture

Nuclear architecture in tissues can arise from cell-type specific organization of nuclear bodies, chromatin states and chromosome structures. However, the lack of genome-wide measurements to interrelate such modalities within single cells limits our overall understanding of nuclear architecture. Here, we demonstrate integrated spatial genomics in the mouse brain cortex, imaging thousands of genomic loci along with RNAs and subnuclear markers simultaneously in individual cells. We revealed chromatin fixed points, combined with cell-type specific organization of nuclear bodies, arrange the interchromosomal organization and radial positioning of chromosomes in diverse cell types. At the sub-megabase level, we uncovered a collection of single-cell chromosome domain structures, including those for the active and inactive X chromosomes. These results advance our understanding of single-cell nuclear architecture in complex tissues.

Unbiased integration of single cell multi-omics data

Acquiring accurate single-cell multiomics profiles often requires performing unbiased in silico integration of data matrices generated by different single-cell technologies from the same biological sample. However, both the rows and the columns can represent different entities in different data matrices, making such integration a computational challenge that has only been solved approximately by existing approaches. Here, we present bindSC, a single-cell data integration tool that realizes simultaneous alignment of the rows and the columns between data matrices without making approximations. Using datasets produced by multiomics technologies as gold standard, we show that bindSC generates accurate multimodal co-embeddings that are substantially more accurate than those generated by existing approaches. Particularly, bindSC effectively integrated single cell RNA sequencing (scRNA-seq) and single cell chromatin accessibility sequencing (scATAC-seq) data towards discovering key regulatory elements in cancer cell-lines and mouse cells. It achieved accurate integration of both common and rare cell types (<0.25% abundance) in a novel mouse retina cell atlas generated using the 10x Genomics Multiome ATAC+RNA kit. Further, it achieves unbiased integration of scRNA-seq and 10x Visium spatial transcriptomics data derived from mouse brain cortex samples. Lastly, it demonstrated efficacy in delineating immune cell types via integrating single-cell RNA and protein data. Thus, bindSC, available at https://github.com/KChen-lab/bindSC, can be applied in a broad variety of context to accelerate discovery of complex cellular and biological identities and associated molecular underpinnings in diseases and developing organisms.

Unbiased integration of single cell multi-omics data

Acquiring accurate single-cell multiomics profiles often requires performing unbiased in silico integration of data matrices generated by different single-cell technologies from the same biological sample. However, both the rows and the columns can represent different entities in different data matrices, making such integration a computational challenge that has only been solved approximately by existing approaches. Here, we present bindSC, a single-cell data integration tool that realizes simultaneous alignment of the rows and the columns between data matrices without making approximations. Using datasets produced by multiomics technologies as gold standard, we show that bindSC generates accurate multimodal co-embeddings that are substantially more accurate than those generated by existing approaches. Particularly, bindSC effectively integrated single cell RNA sequencing (scRNA-seq) and single cell chromatin accessibility sequencing (scATAC-seq) data towards discovering key regulatory elements in cancer cell-lines and mouse cells. It achieved accurate integration of both common and rare cell types (<0.25% abundance) in a novel mouse retina cell atlas generated using the 10x Genomics Multiome ATAC+RNA kit. Further, it achieves unbiased integration of scRNA-seq and 10x Visium spatial transcriptomics data derived from mouse brain cortex samples. Lastly, it demonstrated efficacy in delineating immune cell types via integrating single-cell RNA and protein data. Thus, bindSC, available at https://github.com/KChen-lab/bindSC, can be applied in a broad variety of context to accelerate discovery of complex cellular and biological identities and associated molecular underpinnings in diseases and developing organisms.

An optimized method for the isolation and the molecular characterization of cerebral microvessels in vivo

ABSTRACTThe molecular characterization of cerebral microvessels in experimental disease models has been hindered by the lack of a standardized method to reproducibly isolate intact cerebral microvessels, with consistent cellular compositions, and without the use of enzymatic digestion, which causes undesirable molecular and metabolic changes. Herein, we describe an optimized method for microvessel isolation from mouse brain cortex, which yields microvessel fragments (diameter <50 μm, 89.3% 3-5 μm) with consistent populations of discrete blood-brain barrier components (endothelial cells, pericytes, and astrocyte end feet), retaining high RNA integrity and protein postranslational modifications (e.g. phosphorylation). We demonstrate that this method allows the quantification of changes in gene expression in a disease model (stroke) and the activation of signalling pathways in mice subjected to drug administration. We also describe the isolation of genomic DNA and bisulfite treatment for the assessment of DNA methylation, as well as the optimization of chromatin extraction and shearing from cortical microvessels. Therefore, this protocol will be of great use to improve the understanding of the molecular mechanisms governing cerebrovascular dysfunction, which may help the development of novel therapies for stroke and other neurodegenerative diseases.

Lactate is oxidized outside of the mitochondrial matrix in rodent brain

The nature and existence of mitochondrial lactate oxidation is debated in the literature. Obscuring the issue are disparate findings in isolated mitochondria, as well as relatively low rates of lactate oxidation observed in permeabilized muscle fibres. However, respiration with lactate has yet to be directly assessed in brain tissue with the mitochondrial reticulum intact. To determine if lactate is oxidized in the matrix of brain mitochondria, oxygen consumption was measured in saponin-permeabilized mouse brain cortex samples, and rat prefrontal cortex and hippocampus (dorsal) subregions. While respiration in the presence of ADP and malate increased with the addition of lactate, respiration was maximized following the addition of exogenous NAD+, suggesting maximal lactate metabolism involves extra-matrix lactate dehydrogenase. This was further supported when NAD+-dependent lactate oxidation was significantly decreased with the addition of either low-concentration α-cyano-4-hydroxycinnamate or UK-5099, inhibitors of mitochondrial pyruvate transport. Mitochondrial respiration was comparable between glutamate, pyruvate, and NAD+-dependent lactate oxidation. Results from the current study demonstrate that permeabilized brain is a feasible model for assessing lactate oxidation, and support the interpretation that lactate oxidation occurs outside the mitochondrial matrix in rodent brain.