Biology subjects

Shin, G.

Publications and source records attributed to Shin, G..

5 recordsLinked to original sources

bioRxiv2026

Division-resolved inference of flow and trajectories in proliferating cell populations

High-throughput single-cell assays are widely used to quantify distributions of cell size, morphology, and molecular content across thousands of cells. However, such population distributions do not reveal how the measured cellular states change within individual cells over time. We introduce division-resolved inference of flow and trajectories (DRIFT), a computational framework that infers the dynamics of a measured cellular state from population distributions collected over time, without synchronizing or tracking individual cells. DRIFT solves a population-balance equation to separate state progression from the redistribution caused by cell division in proliferating populations. In simulations of growth and division perturbations, DRIFT recovered the ground-truth mean volume trajectories across simulated single-cell lineages. In live L1210 leukemia cells, DRIFT inferred perturbation-specific volume trajectories that were consistent with longitudinal single-cell measurements. Beyond cell volume, DRIFT also inferred DNA-content dynamics from fixed-cell flow cytometry in L1210 cells, consistent with independent DNA-synthesis assays. In live HeLa cells, DRIFT inferred cell area dynamics that were validated by continuous imaging. Overall, DRIFT converts endpoint measurements of cell populations into division-resolved cellular dynamics, providing a scalable strategy for high-throughput drug-response screening and mechanistic investigation.

systems biology↗

bioRxiv2021

Universal prediction of cell cycle position using transfer learning

BackgroundThe cell cycle is a highly conserved, continuous process which controls faithful replication and division of cells. Single-cell technologies have enabled increasingly precise measurements of the cell cycle both as a biological process of interest and as a possible confounding factor. Despite its importance and conservation, there is no universally applicable approach to infer position in the cell cycle with high-resolution from single-cell RNA-seq data. ResultsHere, we present tricycle, an R/Bioconductor package, to address this challenge by leveraging key features of the biology of the cell cycle, the mathematical properties of principal component analysis of periodic functions, and the use of transfer learning. We estimate a cell cycle embedding using a fixed reference dataset and project new data into this reference embedding; an approach that overcomes key limitations of learning a dataset dependent embedding. Tricycle then predicts a cell-specific position in the cell cycle based on the data projection. The accuracy of tricycle compares favorably to gold-standard experimental assays, which generally require specialized measurements in specifically constructed in vitro systems. Using internal controls which are available for any dataset, we show that tricycle predictions generalize to datasets with multiple cell types, across tissues, species and even sequencing assays. ConclusionsTricycle generalizes across datasets, is highly scalable and applicable to atlas-level single-cell RNA-seq data.

genomics↗

bioRxiv2020

Profiling diverse sequence tandem repeats in colorectal cancer reveals co-occurrence of microsatellite and chromosomal instability involving Chromosome 8

Colorectal carcinomas (CRCs) which have lost DNA mismatch repair display hypermutability evident in a molecular phenotype called microsatellite instability (MSI). These mismatch repair deficient tumors are thought to lack widespread genomic instability features, such as copy number changes and rearrangements. To identify MSI for clinical diagnosis, current molecular testing looks for changes in mononucleotide or dinucleotide repeats. However, microsatellites have other types of sequence tandem repeats such as tri- and tetranucleotide motifs. These additional classes of microsatellites are generally not examined for MSI but are known to be unstable in a phenotype known as elevated microsatellite alterations at selected tetranucleotide repeats, or EMAST. We developed a sequencing approach that provides ultra-high coverage (>2500X) of microsatellite targets and cancer genes for profiling genomic instability. We assessed the diverse repeat motifs across 200 microsatellites. Our approach provides highly sensitive detection of MSI with high specificity, evaluates copy number alterations with high accuracy, delineates chromosomal instability (CIN) classification and deconvolutes subclonal architecture. By examining both MSI and CIN, we discovered mutations and copy number alterations that defined mixed genomic instability states of CIN and MSI, which are normally considered exclusive. An increase in copy number of chromosome arm 8q was prevalent among MSI tumors. Moreover, we identified an inter-chromosomal translocation event from a CRC with co-occurrence of MSI. Subclonal analysis demonstrated that mutations which are typically considered to be exclusive in MSI, shows mutual occurrence in MSI tumors with more sensitive characterization. Our approach revealed that MSH3 mutations are a potential source of mixed genomic instability features. Overall, our study demonstrates that some colorectal cancers have features of both microsatellite and chromosomal instability. This result may have implications for immunotherapy treatment.

genomics↗

bioRxiv2020

Complete and haplotype-specific sequence assembly of segmental duplication-mediated genome rearrangements using targeted CRISPR-targeted ultra-long read sequencing (CTLR-Seq)

We have developed a generally applicable method based on CRISPR/Cas9-targeted ultra-long read sequencing (CTLR-Seq) to completely and haplotype-specifically resolve, at base-pair resolution, large, complex, and highly repetitive genomic regions that had been previously impenetrable to next-generation sequencing analysis such as large segmental duplication (SegDup) regions and their associated genome rearrangements that stretch hundreds of kilobases. Our method combines in vitro Cas9-mediated cutting of the genome and pulse-field gel electrophoresis to haplotype-specifically isolate intact large (200-550 kb) target regions that encompass previously unresolvable genomic sequences. These target fragments are then sequenced (amplification-free) to produce ultra-long reads at up to 40x on-target coverage using Oxford nanopore technology, allowing for the complete assembly of the complex genomic regions of interest at single base-pair resolution. We applied CTLR-Seq to resolve the exact sequence of SegDup rearrangements that constitute the boundary regions of the 22q11.2 deletion CNV and of the 16p11.2 deletion and duplication CNVs. These CNVs are among the strongest known risk factors for schizophrenia and autism. We then perform de novo assembly to resolve, for the first time, at single base-pair resolution, the sequence rearrangements of the 22q11.2 and 16p11.2 CNVs, mapping out exactly the genes and non-coding regions that are affected by the CNV for different carriers.

genomics↗

bioRxiv2018

Assembly of Mb-size genome segments from linked read sequencing of CRISPR DNA targets

We developed a targeted sequencing method for intact high molecular weight (HMW) DNA targets as large as 0.2 Mb. This process uses HMW DNA isolated from intact cells, custom designed Cas9-guide RNA complexes to generate 0.1 - 0.2 Mb DNA targets, electrophoretic isolation of the DNA targets and sequencing with barcode linked reads. We used alignment methods as well as local assembly of the target regions to identify haplotypes and structural variants (SVs) across multi-Megabase genomic regions. To demonstrate the performance of this approach, we designed three assays that covered a 0.2 Mb region surrounding the BRCA1 gene, a set of 40 overlapping 0.2 Mb targets covering the entire 4-Mb MHC locus, and 18 well-characterized structural variants. Using the highly characterized NA12878 genome, we achieved on-target coverage of more than 50X, while overall whole genome coverage was approximately 4X. We generated haplotypes that completely covered each targeted locus, with a maximum size of 4 Mb (for the MHC region). This method detected structural variants such as deletions and inversions with determination of the exact breakpoints and genotypes. Even breakpoints inside highly homologous segmental duplications are precisely determined with our high-quality assemblies. Overall, this is a new method to sequence large DNA segments.

genomics↗