Biology subjects

Chou, N.

Publications and source records attributed to Chou, N..

3 recordsLinked to original sources

bioRxiv2025

Guidestar: a spike-in approach to improve RNA detection accuracy in imaging-based spatial transcriptomics

Imaging-based spatial transcriptomics technologies, such as MERFISH and seq-FISH, use combinatorial barcoding and imaging to simultaneously detect individual RNA molecules from 10s to 10,000s of genes. These technologies require the decoding of individual RNA molecules location and gene identity from stacks of images. However, beyond using blank code-words as negative controls, there is a lack of ground truth information embedded within the assay to experimentally measure the accuracy and sensitivity of the decoding algorithm. We introduce Guidestar, a system of spike-in controls integrated within a combinatorial FISH assay, that labels a subset of RNA transcripts with additional probes. These probes are imaged separately as guide bits, which serve as ground-truth data to assess decoding accuracy at the level of individual RNA molecules. Using Guidestar to evaluate accuracy of an existing decoding method suggested alternative parameter settings that increased sensitivity with minimal impact on accuracy. We also used the Guidestar dataset to train a machine-learning based classifier to distinguish true from false RNA calls, yielding 9% and 40% higher F1 scores across cell line and tissue samples, respectively.

genomics↗

bioRxiv2023

Highly sensitive spatial transcriptomics using FISHnCHIPs of multiple co-expressed genes

High-dimensional, spatially resolved analysis of intact tissue samples promises to transform biomedical research and diagnostics, but existing spatial omics technologies are costly and labor-intensive. We present FISHnCHIPs for highly sensitive in situ profiling of cell types and gene expression programs. FISHnCHIPs achieves this by simultaneously imaging [~]2-35 co-expressed genes that are spatially co-localized in tissues, resulting in similar spatial information as single-gene FISH, but at [~]2-20-fold higher sensitivity. Using FISHnCHIPs, we imaged up to 53 gene modules from the mouse kidney and mouse brain, and demonstrated high-speed, large field-of-view profiling of a whole tissue section. FISHnCHIPS also revealed spatially restricted localizations of cancer-associated fibroblasts in a human colorectal cancer biopsy. Overall, FISHnCHIPs enables robust and scalable spatial transcriptomics analysis of tissues with normal physiology or undergoing pathogenesis.

genomics↗

bioRxiv2022

BANKSY: A Spatial Omics Algorithm that Unifies Cell Type Clustering and Tissue Domain Segmentation

Each cell type in a solid tissue has a characteristic transcriptome and spatial arrangement, both of which are observable using modern spatial omics assays. However, the common practice is still to ignore spatial information when clustering cells to identify cell types. In fact, spatial location is typically considered only when solving the related, but distinct, problem of demarcating tissue domains (which could include multiple cell types). We present BANKSY, an algorithm that unifies cell type clustering and domain segmentation by constructing a product space of cell and neighbourhood transcriptomes, representing cell state and microenvironment, respectively. BANKSYs spatial kernel-based feature augmentation strategy improves per-formance and scalability on both tasks when tested on FISH-based and sequencing-based spatial omics data. Uniquely, BANKSY identified hitherto undetected niche-dependent cell states in two mouse brain regions. Lastly, we show that quality control of spatial omics data can be formulated as a domain identification problem and solved using BANKSY. BANKSY represents a biologically motivated, scalable, and versatile framework for analyzing spatial omics data.

bioinformatics↗