Biology subjects

Bryan, J. P.

Publications and source records attributed to Bryan, J. P..

3 recordsLinked to original sources

bioRxiv2025

Accurate trajectory inference in time-series spatial transcriptomics with structurally-constrained optimal transport

New experimental and computational methods use genetic or gene expression observations with single cell resolution to study the relationship between single-cell molecular profiles and developmental trajectories. Most tissues contain spatially contiguous regions that develop as a unit, such as follicles in the ovary, or tubules and glomeruli in the kidney. We find that existing approaches designed to use time series spatial transcriptomics (ST) data produce biologically incoherent trajectories that fail to maintain these structural units over time. We present Spatiotemporal Optimal transport with Contiguous Structures (SOCS), an Optimal Transport-based trajectory inference method for time-series ST that produces trajectory inferences preserving the structural integrity of contiguous biologically meaningful units, along with gene expression similarity and global geometric structure. We show that SOCS produces more plausible trajectory estimates, maintaining the spatial coherence of biological structures across time, enabling more accurate trajectory inference and biological insight than other approaches.

bioinformatics↗

bioRxiv2024

Single-cell and spatiotemporal profile of ovulation in the mouse ovary

Ovulation is a spatiotemporally coordinated process that involves several tightly controlled events, including oocyte meiotic maturation, cumulus expansion, follicle wall rupture and repair, and ovarian stroma remodeling. To date, no studies have detailed the precise window of ovulation at single-cell resolution. Here, we performed parallel single-cell RNA-seq and spatial transcriptomics on paired mouse ovaries across an ovulation time course to map the spatiotemporal profile of ovarian cell types. We show that major ovarian cell types exhibit time-dependent transcriptional states enriched for distinct functions and have specific localization profiles within the ovary. We also identified gene markers for ovulation-dependent cell states and validated these using orthogonal methods. Finally, we performed cell-cell interaction analyses to identify ligand-receptor pairs that may drive ovulation, revealing previously unappreciated interactions. Taken together, our data provides a rich and comprehensive resource of murine ovulation that can be mined for discovery by the scientific community.

bioinformatics↗

bioRxiv2022

Optimization-Based Decoding of Imaging Spatial Transcriptomics Data

MotivationImaging Spatial Transcriptomics (iST) techniques characterize gene expression in cells in their native context by imaging barcoded probes for mRNA with single molecule resolution. However, the need to acquire many rounds of high-magnification imaging data limits the throughput and impact of existing methods. ResultsWe describe the Joint Sparse method for Imaging Transcriptomics (JSIT), an algorithm for decoding lower magnification IT data than that used in standard experimental workflows. JSIT incorporates codebook knowledge and sparsity assumptions into an optimization problem which is less reliant on well separated optical signals than current pipelines. Using experimental data obtained by performing Multiplexed Error-Robust Fluorescence in situ Hybridization (MERFISH) on tissue from mouse motor cortex, we demonstrate that JSIT enables improved throughput and recovery performance over standard decoding methods. Contactyonina.eldar@weizmann.ac.il, sfarhi@broadinstitute.org, bcleary@bu.edu Availability and ImplementationSoftware implementation of JSIT, together with example files, are available at https://github.com/jpbryan13/JSIT. Supplementary InformationSupplementary data are available at Bioinformatics online.

bioinformatics↗