Biology subjects

Govek, K. W.

Publications and source records attributed to Govek, K. W..

4 recordsLinked to original sources

bioRxiv2022

A general framework for the combined morphometric, transcriptomic, and physiological analysis of cells using metric geometry

High-resolution imaging-based single-cell profiling has transformed the study of cells in their spatial context. However, the lack of quantitative methods that can summarize the great diversity of complex cell shapes found in tissues and infer associations with other single-cell data modalities limits current analyses. Here, we report a general computational framework for the multi-modal analysis and integration of single-cell morphological data. We build upon metric geometry to construct cell morphology latent spaces, where distances in these spaces indicate the amount of physical deformation needed to change the morphology of one cell into that of another. Using these spaces, we integrate morphological data across technologies and leverage associated single-cell RNA-seq data to infer relations between morphological and transcriptomic cellular processes. We apply this framework to imaging and multi-modal data of neurons and glia to uncover genes related to neuronal plasticity. Our approach represents a strategy for incorporating cell morphological data into single-cell omics analyses.

bioinformatics↗

bioRxiv2022

Combined single-cell RNA-seq profiling and enhancer editing reveals critical spatiotemporal controls over thalamic nuclei formation in the murine embryo

The thalamus is the principal information hub of the vertebrate brain, with essential roles in sensory and motor information processing, attention, and memory. The molecular mechanisms regulating the formation of thalamic nuclei are unclear. We apply longitudinal single-cell RNA-sequencing, regional abrogation of Sonic-hedgehog (Shh), and spatial profiling of gene expression to map the developmental trajectories of thalamic progenitors, intermediate progenitors, and post-mitotic neurons as they coalesce into distinct thalamic nuclei. These data reveal that the complex architecture of the thalamus is established early during embryonic brain development through the coordinated action of four cell differentiation lineages derived from Shh-dependent and independent progenitors. We systematically characterize the gene expression programs that define these lineages across time and demonstrate how their disruption upon Shh depletion causes pronounced locomotor impairment resembling infantile Parkinsons disease. These results reveal key principles of thalamic development and provide mechanistic insights into neurodevelopmental disorders resulting from thalamic dysfunction.

neuroscience↗

bioRxiv2019

Massively parallel, time-resolved single-cell RNA sequencing with scNT-Seq

Single-cell RNA sequencing offers snapshots of whole transcriptomes but obscures the temporal dynamics of RNA biogenesis and decay. Here we present single-cell new transcript tagging sequencing (scNT-Seq), a method for massively parallel analysis of newly-transcribed and pre-existing RNAs from the same cell. This droplet microfluidics-based method enables high-throughput chemical conversion on barcoded beads, efficiently marking metabolically labeled newly-transcribed RNAs with T-to-C substitutions. By simultaneously measuring new and old transcriptomes, scNT-Seq reveals neuronal subtype-specific gene regulatory networks and time-resolved RNA trajectories in response to brief (minutes) versus sustained (hours) neuronal activation. Integrating scNT-Seq with genetic perturbation reveals that DNA methylcytosine dioxygenases may inhibit stepwise transition from pluripotent embryonic stem cell state to intermediate and totipotent two-cell-embryo-like (2C-like) states by promoting global RNA biogenesis. Furthermore, pulse-chase scNT-Seq enables transcriptome-wide measurements of RNA stability in rare 2C-like cells. Time-resolved single-cell transcriptomic analysis thus opens new lines of inquiry regarding cell-type-specific RNA regulatory mechanisms.

genomics↗

bioRxiv2019

Single-Cell Transcriptomic Analysis of mIHC Images via Antigen Mapping

Highly-multiplexed immunohistochemistry (mIHC) enables the staining and quantification of dozens of antigens in a tissue section with single-cell resolution. However, annotating cell populations that differ little in the profiled antigens or for which the antibody panel does not include specific markers is challenging. To overcome this obstacle, we have developed an approach for enriching mIHC images with single-cell RNA-seq data, building upon recent experimental procedures for augmenting single-cell transcriptomes with concurrent antigen measurements. Spatially-resolved Transcriptomics via Epitope Anchoring (STvEA) performs transcriptome-guided annotation of highly-multiplexed cytometry datasets. It increases the level of detail in histological analyses by enabling annotation of subtle cell populations, spatial patterns of transcription, and interactions between cell types. More generally, it enables the systematic annotation of cell populations in cytometry data. We demonstrate the utility of STvEA by uncovering the architecture of poorly characterized cell types in the murine spleen using published highly-multiplexed cytometry and mIHC data.

bioinformatics↗