Biology subjects

Sritharan, D.

Publications and source records attributed to Sritharan, D..

4 recordsLinked to original sources

bioRxiv2021

Droplet-based single cell RNA sequencing of bacteria identifies known and previously unseen cellular states

Clonal bacterial populations rely on transcriptional variation to differentiate into specialized cell states that increase the communitys fitness. Such heterogeneous gene expression is implicated in many fundamental microbial processes including sporulation, cell communication, detoxification, substrate utilization, competence, biofilm formation, motility, pathogenicity, and antibiotic resistance1. To identify these specialized cell states and determine the processes by which they develop, we need to study isogenic bacterial populations at the single cell level2,3. Here, we develop a method that uses DNA probes and leverages an existing commercial microfluidic platform (10X Chromium) to conduct bacterial single cell RNA sequencing. We sequenced the transcriptome of over 15,000 individual bacterial cells, detecting on average 365 transcripts mapping to 265 genes per cell in B. subtilis and 329 transcripts mapping to 149 genes per cell in E. coli. Our findings correctly identify known cell states and uncover previously unreported cell states. Interestingly, we find that some metabolic pathways segregate into distinct subpopulations across different bacteria and growth conditions, suggesting that some cellular processes may be more prone to differentiation than others. Our high throughput, highly resolved single cell transcriptomic platform can be broadly used for understanding heterogeneity in microbial populations.

microbiology↗

bioRxiv2021

Computing the Riemannian curvature of image patch and single-cell RNA sequencing data manifolds using extrinsic differential geometry

Most high-dimensional datasets are thought to be inherently low-dimensional, that is, datapoints are constrained to lie on a low-dimensional manifold embedded in a high-dimensional ambient space. Here we study the viability of two approaches from differential geometry to estimate the Riemannian curvature of these low-dimensional manifolds. The intrinsic approach relates curvature to the Laplace-Beltrami operator using the heat-trace expansion, and is agnostic to how a manifold is embedded in a high-dimensional space. The extrinsic approach relates the ambient coordinates of a manifolds embedding to its curvature using the Second Fundamental Form and the Gauss-Codazzi equation. Keeping in mind practical constraints of real-world datasets, like small sample sizes and measurement noise, we found that estimating curvature is only feasible for even simple, low-dimensional toy manifolds, when the extrinsic approach is used. To test the applicability of the extrinsic approach to real-world data, we computed the curvature of a well-studied manifold of image patches, and recapitulated its topological classification as a Klein bottle. Lastly, we applied the approach to study single-cell transcriptomic sequencing (scRNAseq) datasets of blood, gastrulation, and brain cells, revealing for the first time the intrinsic curvature of scRNAseq manifolds.

bioinformatics↗

bioRxiv2019

An engineered CRISPR/Cas9 mouse line for simultaneous readout of lineage histories and gene expression profiles in single cells

Tracing the lineage history of cells is key to answering diverse and fundamental questions in biology. Particularly in the context of stem cell biology, analysis of single cell lineages in their native state has elucidated novel fates and highlighted heterogeneity of function. Coupling of such ancestry information with other molecular readouts represents an important goal in the field. Here, we describe the CARLIN (for CRISPR Array Repair LINeage tracing) mouse line and corresponding analysis tools that can be used to simultaneously interrogate the lineage and transcriptomic information of single cells in vivo. This model exploits CRISPR technology to generate up to 44,000 transcribed barcodes in an inducible fashion at any point during development or adulthood, is compatible with sequential barcoding, and is fully genetically defined. We have used CARLIN to identify intrinsic biases in the activity of fetal liver hematopoietic stem cell (HSC) clones and to uncover a previously unappreciated clonal bottleneck in the response of HSCs to injury. CARLIN also allows the unbiased identification of transcriptional signatures based on in vivo stem cell function without a need for markers or cell sorting.

developmental biology↗

bioRxiv2018

Atomic-level characterization of protein-protein association

Despite the biological importance of protein-protein complexes, determining their structures and association mechanisms remains an outstanding challenge. Here, we report the results of atomic-level simulations in which we observed five protein-protein pairs repeatedly associate to, and dissociate from, their experimentally determined native complexes using a new molecular dynamics (MD)-based sampling approach that does not make use of any prior structural information about the complexes. To study association mechanisms, we performed additional, conventional MD simulations, in which we observed numerous spontaneous association events. A shared feature of native association for these five structurally and functionally diverse protein systems was that if the proteins made contact far from the native interface, the native state was reached by dissociation and eventual re-association near the native interface, rather than by extensive interfacial exploration while the proteins remained in contact. At the transition state (the conformational ensemble from which association to the native complex and dissociation are equally likely), the protein-protein interfaces were still highly hydrated, and no more than 20% of native contacts had formed.

biophysics↗