Biology subjects

Chiang, Z. D.

Publications and source records attributed to Chiang, Z. D..

3 recordsLinked to original sources

bioRxiv2026

Unified Transcriptome and Mechanics Map of the Intact Mammalian Preimplantation Embryo In Situ

The development and maintenance of multicellular tissues requires that cell states be closely coupled to their local environment, including geometric and mechanical cues. However, studying this coupling in intact tissues has been challenging, because existing measurement technologies cannot simultaneously assess mechanical properties and molecularly defined cell states. To address this gap, we introduce the Unified Transcriptome and Mechanics Map (UTMM), a method for concurrent measurement of the transcriptome and cytoplasmic stiffness (high-frequency elastic modulus) within intact 3D multicellular structures. UTMM relies on two innovations: (1) a targeted in situ RNA sequencing approach for intact 3D embryos (3DISS), and (2) a strategy that leverages high-frequency intracellular organelle fluctuations to infer cell-level stiffness in vivo. We applied UTMM to mouse embryos from the zygote through the morula stage to characterize how RNA expression and cytoplasmic stiffness become coupled during early lineage specification. Our data reveal that, in the early morula, transcriptional and morphological distinctions emerge between trophectoderm and inner cell mass (ICM) lineages, coinciding with a gradual decrease in cytoplasmic stiffness (softening) across all cells from the 2-cell through morula stages. Furthermore, we observe that early lineage biases align with differential mechanical properties, reflecting distinct emerging developmental programs. When we delayed this softening process via mechanical perturbation, embryonic progression was impeded, highlighting the functional importance of coordinated mechanical and transcriptional changes. Together, these results demonstrate UTMMs ability to bridge molecular and mechanical dynamics in multicellular systems, providing a powerful framework for investigating how biomechanical cues shape cell fate decisions in intact tissues. One Sentence SummaryJoint 3D in situ quantification of spatial transcriptome and cytoplasm mechanics in preimplantation mammalian embryos

developmental biology↗

bioRxiv2024

Expansion in situ genome sequencing links nuclear abnormalities to hotspots of aberrant euchromatin repression

Microscopy and genomics are both used to characterize cell function, but approaches to connect the two types of information are lacking, particularly at subnuclear resolution. While emerging multiplexed imaging methods can simultaneously localize genomic regions and nuclear proteins, their ability to accurately measure DNA-protein interactions is constrained by the diffraction limit of optical microscopy. Here, we describe expansion in situ genome sequencing (ExIGS), a technology that enables sequencing of genomic DNA and superresolution localization of nuclear proteins in single cells. We applied ExIGS to fibroblast cells derived from an individual with Hutchinson-Gilford progeria syndrome to characterize how variation in nuclear morphology affects spatial chromatin organization. Using this data, we discovered that lamin abnormalities are linked to hotspots of aberrant euchromatin repression that may erode cell identity. Further, we show that lamin abnormalities heterogeneously increase the repressive environment of the nucleus in tissues and aged cells. These results demonstrate that ExIGS may serve as a generalizable platform for connecting nuclear abnormalities to changes in gene regulation across disease contexts.

genomics↗

bioRxiv2019

AtacWorks: A deep convolutional neural network toolkit for epigenomics

We introduce AtacWorks (https://github.com/clara-genomics/AtacWorks), a method to denoise and identify accessible chromatin regions from low-coverage or low-quality ATAC-seq data. AtacWorks uses a deep neural network to learn a mapping between noisy ATAC-seq data and corresponding higher-coverage or higher-quality data. To demonstrate the utility of AtacWorks, we train a model on data from four human blood cell types and show that this model accurately denoises chromatin accessibility at base-pair resolution and identifies peaks from low-coverage bulk sequencing of unseen cell types and experimental conditions. We use the same framework to obtain high-quality results from as few as 50 aggregate single-cell ATAC-seq profiles, and also from data with a low signal-to-noise ratio. We further show that AtacWorks can be adapted for cross-modality prediction of transcription factor footprints and ChIP-seq peaks from low input ATAC-seq. Finally, we demonstrate the applications of our approach to single-cell genomics by using AtacWorks to identify regulatory regions that are differentially-accessible between rare lineage-primed subpopulations of hematopoietic stem cells.

genomics↗