Biology subjects

Tsia, K.

Publications and source records attributed to Tsia, K..

5 recordsLinked to original sources

bioRxiv2024

StaVia: Spatially and temporally aware cartography with higher order random walks for cell atlases

Single-cell atlases are critical for unraveling the cellular basis of health and disease, yet their sheer diversity and vast data landscape in time and space pose daunting computational challenges pertaining to the delineation of multiple simultaneously emerging lineages, the integration of spatial and temporal information, and the visualization of trajectories across large atlases with enough resolution to observe localized transitions. To tackle this intricacy, we introduce StaVia, a computational framework that synergizes multi-faceted single-cell data--spanning time-series data, spatial gene expression patterns, and directional trends from RNA velocity-- with higher-order random walks that leverage the memory of cells past states. StaVia fuses this method with a cartographic "Atlas View" that offers intuitive graph visualization, simultaneously capturing the nuanced details of cellular development at single-cell resolution as well as the broader connectivity of cell lineages, avoiding common pitfalls of merged distinct trajectories or missed transitional states seen in existing methods which are all memoryless. Notably, we demonstrate that StaVia unlocks new insights into placode development, radial glia pluripotency during neurulation, and the transitions pivotal to these processes in a large-scale Zebrafish developmental atlas. StaVia also allows spatially aware cartography that captures relationships between cell populations based on their spatial location as well as their gene expressions in a MERFISH dataset - underscoring its potential to dissect complex biological landscapes in both spatial and temporal contexts.

bioinformatics↗

bioRxiv2021

VIA: Generalized and scalable trajectory inference in single-cell omics data

Inferring cellular trajectories using a variety of omic data is a critical task in single-cell data science. However, accurate prediction of cell fates, and thereby biologically meaningful discovery, is challenged by the sheer size of single-cell data, the diversity of omic data types, and the complexity of their topologies. We present VIA, a scalable trajectory inference algorithm that overcomes these limitations by using lazy-teleporting random walks to accurately reconstruct complex cellular trajectories beyond tree-like pathways (e.g. cyclic or disconnected structures). We show that VIA robustly and efficiently unravels the fine-grained sub-trajectories in a 1.3-million-cell transcriptomic mouse atlas without losing the global connectivity at such a high cell count. We further apply VIA to discovering elusive lineages and less populous cell fates missed by other methods across a variety of data types, including single-cell proteomic, epigenomic, multi-omics datasets, and a new in-house single-cell morphological dataset.

bioinformatics↗

bioRxiv2019

PARC: ultrafast and accurate clustering of phenotypic data of millions of single cells

MotivationNew single-cell technologies continue to fuel the explosive growth in the scale of heterogeneous single-cell data. However, existing computational methods are inadequately scalable to large datasets and therefore cannot uncover the complex cellular heterogeneity.\n\nResultsWe introduce a highly scalable graph-based clustering algorithm PARC - phenotyping by accelerated refined community-partitioning - for ultralarge-scale, high-dimensional single-cell data (> 1 million cells). Using large single cell mass cytometry, RNA-seq and imaging-based biophysical data, we demonstrate that PARC consistently outperforms state-of-the-art clustering algorithms without sub-sampling of cells, including Phenograph, FlowSOM, and Flock, in terms of both speed and ability to robustly detect rare cell populations. For example, PARC can cluster a single cell data set of 1.1M cells within 13 minutes, compared to >2 hours to the next fastest graph-clustering algorithm, Phenograph. Our work presents a scalable algorithm to cope with increasingly large-scale single-cell analysis.\n\nAvailability and Implementationhttps://github.com/ShobiStassen/PARC

bioinformatics↗

bioRxiv2019

Kilohertz in vivo imaging of neural activity

Understanding information processing in the brain requires us to monitor neural activity in vivo at high spatiotemporal resolution. Using an ultrafast two-photon fluorescence microscope (2PFM) empowered by all-optical laser scanning, we imaged neural activity in vivo at up to 3,000 frames per second and submicron spatial resolution. This ultrafast imaging method enabled monitoring of both supra- and sub-threshold electrical activity down to 345 m below the brain surface in head fixed awake mice.

neuroscience↗

bioRxiv2019

Multi-ATOM: Ultrahigh-throughput single-cell quantitative phase imaging with subcellular resolution

A growing body of evidence has substantiated the significance of quantitative phase imaging (QPI) in enabling cost-effective and label-free cellular assay, which provides useful insights into understanding biophysical properties of cells and their roles in cellular functions. However, available QPI modalities are limited by the loss of imaging resolution at high throughput and thus run short of sufficient statistical power at the single cell precision to define cell identities in a large and heterogeneous population of cells - hindering their utility in mainstream biomedicine and biology. Here we present a new QPI modality, coined multi-ATOM that captures and processes quantitative label-free single-cell images at ultra-high throughput without compromising sub-cellular resolution. We show that multi-ATOM, based upon ultrafast phase-gradient encoding, outperforms state-of-the-art QPI in permitting robust phase retrieval at a QPI throughput of >10,000 cell/sec, bypassing the need for interferometry which inevitably compromises QPI quality under ultrafast operation. We employ multi-ATOM for large-scale, label-free, multi-variate, cell-type classification (e.g. breast cancer sub-types, and leukemic cells versus peripheral blood mononuclear cells) at high accuracy (>94%). Our results suggest that multi-ATOM could empower new strategies in large-scale biophysical single-cell analysis with applications in biology and enriching disease diagnostics.

bioengineering↗