Biology subjects

Hsieh, Y.-H.

Publications and source records attributed to Hsieh, Y.-H..

4 recordsLinked to original sources

bioRxiv2024

Transcript-specific enrichment enables profiling rare cell states via scRNA-seq

Single-cell genomics technologies have accelerated our understanding of cell-state heterogeneity in diverse contexts. Although single-cell RNA sequencing (scRNA-seq) identifies many rare populations of interest that express specific marker transcript combinations, traditional flow sorting limits our ability to enrich these populations for further profiling, including requiring cell surface markers with high-fidelity antibodies. Additionally, many single-cell studies require the isolation of nuclei from tissue, eliminating the ability to enrich learned rare cell states based on extranuclear protein markers. To address these limitations, we describe Programmable Enrichment via RNA Flow-FISH by sequencing (PERFF-seq), a scalable assay that enables scRNA-seq profiling of subpopulations from complex cellular mixtures defined by the presence or absence of specific RNA transcripts. Across immune populations (n = 141,227 cells) and fresh-frozen and formalin-fixed paraffin-embedded brain tissue (n = 29,522 nuclei), we demonstrate the sorting logic that can be used to enrich for cell populations via RNA-based cytometry followed by high-throughput scRNA-seq. Our approach provides a rational, programmable method for studying rare populations identified by one or more marker transcripts.

genomics↗

bioRxiv2024

Cross-institutional HER2 assessment via a computer-aided system using federated learning and stain composition augmentation

The rapid advancement of precision medicine and personalized healthcare has heightened the demand for accurate diagnostic tests. These tests are crucial for administering novel treatments like targeted therapy. To ensure the widespread availability of accurate diagnostics with consistent standards, the integration of computer-aided systems has become essential. Specifically, computer-aided systems that assess biomarker expression have thrusted through the widespread application of deep learning for medical imaging. However, the generalizability of deep learning models has usually diminished significantly when being confronted with data collected from different sources, especially for histological imaging in digital pathology. It has therefore been challenging to effectively develop and employ a computer-aided system across multiple medical institutions. In this study, a biomarker computer-aided framework was proposed to overcome such challenges. This framework incorporated a new approach to augment the composition of histological staining, which enhanced the performance of federated learning models. A HER2 assessment system was developed following the proposed framework, and it was evaluated on a clinical dataset from National Taiwan University Hospital and a public dataset coordinated by the University of Warwick. This assessment system showed an accuracy exceeding 90% for both institutions, whose generalizability outperformed a baseline system developed solely through the clinical dataset by 30%. Compared to previous works where data across different institutions were mixed during model training, the HER2 assessment system achieved a similar performance while it was developed with guaranteed patient privacy via federated learning.

pathology↗

bioRxiv2023

Codon affinity in mitochondrial DNA shapes evolutionary and somatic fitness

Summary ParagraphSomatic variation contributes to biological heterogeneity by modulating cellular proclivity to differentiate, expand, adapt, or die. While large-scale sequencing efforts have revealed the foundational role of somatic variants to drive human tumor evolution, our understanding of the contribution of mutations to modulate cellular fitness in non-malignant contexts remains understudied. Here, we identify a mosaic synonymous variant (m.7076A>G) in the mitochondrial DNA (mtDNA) encoded cytochrome c-oxidase subunit 1 gene (MT-CO1, p.Gly391=), which was present at homoplasmy in 47% of immune cells from a healthy donor. Using single-cell multi-omics, we discover highly specific selection against the m.7076G mutant allele in the CD8+ effector memory T cell compartment in vivo, reminiscent of selection observed for pathogenic mtDNA alleles1, 2 and indicative of lineage-specific metabolic requirements. While the wildtype m.7076A allele is translated via Watson-Crick-Franklin base-pairing, the anticodon diversity of the mitochondrial transfer RNA pool is limited, requiring wobble-dependent translation of the m.7076G mutant allele. Notably, mitochondrial ribosome profiling revealed altered codon-anticodon affinity at the wobble position as evidenced by stalled translation of the synonymous m.7076G mutant allele encoding for glycine. Generalizing this observation, we provide a new ontogeny of the 8,482 synonymous variants in the human mitochondrial genome that enables interpretation of functional mtDNA variation. Specifically, via inter- and intra-species evolutionary analyses, population-level complex trait associations, and the occurrence of germline and somatic mtDNA mutations from large-scale sequencing studies, we demonstrate that synonymous variation impacting codon:anticodon affinity is actively evolving across the entire mitochondrial genome and has broad functional and phenotypic effects. In summary, our results introduce a new ontogeny for mitochondrial genetic variation and support a model where organismal principles can be discerned from somatic evolution via single-cell genomics.

genetics↗

bioRxiv2022

Single-cell multi-omics reveals dynamics of purifying selection of pathogenic mitochondrial DNA across human immune cells

Cells experience intrinsic and extrinsic pressures that affect their proclivity to expand and persist in vivo. In congenital disorders caused by loss-of-function mutations in mitochondrial DNA (mtDNA), metabolic vulnerabilities may result in cell-type specific phenotypes and depletion of pathogenic alleles, contributing to purifying selection. However, the impact of pathogenic mtDNA mutations on the cellular hematopoietic landscape is not well understood. Here, we establish a multi-omics approach to quantify deletions in mtDNA alongside cell state features in single cells derived from Pearson syndrome patients. We resolve the interdependence between pathogenic mtDNA and lineage, including purifying selection against deletions in effector/memory CD8 T-cell populations and recent thymic emigrants and dynamics in other hematopoietic populations. Our mapping of lineage-specific purifying selection dynamics in primary cells from patients carrying pathogenic heteroplasmy provides a new perspective on recurrent clinical phenotypes in mitochondrial disorders, including cancer and infection, with potential broader relevance to age-related immune dysfunction.

genetics↗