Biology subjects

Lam, E. T.

Publications and source records attributed to Lam, E. T..

5 recordsLinked to original sources

bioRxiv2025

Tyrosine kinase inhibitors affect sweet taste and dysregulate fate selection of specific taste cell subtypes via KIT inhibition.

Taste dysfunction, or dysgeusia, is a common side effect of many cancer drugs. Dysgeusia is often reported by patients treated with anti-angiogenic tyrosine kinase inhibitors (TKIs), which inhibit receptor tyrosine kinases (RTKs). However, the mechanisms by which TKIs cause dysgeusia are not understood, as the role of RTKs in adult taste homeostasis is unknown. Here, we find that treating adult mice with the TKI cabozantinib shifts the fate of differentiating functional taste cell subtypes within taste buds. Through behavioral assays, we find this cell fate shift leads to blunted responses to sweet in cabozantinib-treated mice. Finally, we show that inducible knockout of the RTK KIT, which is inhibited by cabozantinib, phenocopies taste cell fate shifts induced by TKI treatment. Our results establish KIT as a regulator of taste cell homeostasis and suggest that KIT inhibition may underlie TKI-induced dysgeusia in patients. SummaryKIT signaling blockade by tyrosine kinase inhibitors alters the fate of functional taste bud cell subtypes.

neuroscience↗

bioRxiv2025

Quantum Cognition Machine Learning for Forecasting Chromosomal Instability

The accurate prediction of chromosomal instability from the morphology of circulating tumor cells (CTCs) enables real-time detection of CTCs with high metastatic potential in the context of liquid biopsy diagnostics. However, it presents a significant challenge due to the high dimensionality and complexity of single-cell digital pathology data. Here, we introduce the application of Quantum Cognition Machine Learning (QCML), a quantum-inspired computational framework, to estimate morphology-predicted chromosomal instability in CTCs from patients with metastatic breast cancer. QCML leverages quantum mechanical principles to represent data as state vectors in a Hilbert space, enabling context-aware feature modeling, dimensionality reduction, and enhanced generalization without requiring curated feature selection. QCML outperforms conventional machine learning methods when tested on out of sample verification CTCs, achieving higher accuracy in identifying predicted large-scale state transitions (pLST) status from CTC-derived morphology features. These preliminary findings support the application of QCML as a novel machine learning tool with superior performance in high-dimensional, low-sample-size biomedical contexts. QCML enables the simulation of cognition-like learning for the identification of biologically meaningful prediction of chromosomal instability from CTC morphology, offering a novel tool for CTC classification in liquid biopsy.

cancer biology↗

bioRxiv2018

CRISPR-bind: a simple, custom CRISPR/dCas9-mediated labeling of genomic DNA for mapping in nanochannel arrays

Bionano genome mapping is a robust optical mapping technology used for de novo construction of whole genomes using ultra-long DNA molecules, able to efficiently interrogate genomic structural variation. It is also used for functional analysis such as epigenetic analysis and DNA replication mapping and kinetics. Genomic labeling for genome mapping is currently specified by a single strand nicking restriction enzyme followed by fluorophore incorporation by nick-translation (NLRS), or by a direct label and stain (DLS) chemistry which conjugates a fluorophore directly to an enzyme-defined recognition site. Although these methods are efficient and produce high quality whole genome mapping data, they are limited by the number of available enzymes--and thus the number of recognition sequences--to choose from. The ability to label other sequences can provide higher definition in the data and may be used for countless additional applications. Previously, custom labeling was accomplished via the nick-translation approach using CRISPR-Cas9, leveraging Cas9 mutant D10A which has one of its cleavage sites deactivated, thus effectively converting the CRISPR-Cas9 complex into a nickase with customizable target sequences. Here we have improved upon this approach by using dCas9, a nuclease-deficient double knockout Cas9 with no cutting activity, to directly label DNA with a fluorescent CRISPR-dCas9 complex (CRISPR-bind). Unlike labeling with CRISPR-Cas9 D10A nickase, in which nicking, labeling, and repair by ligation, all occur as separate steps, the new assay has the advantage of labeling DNA in one step, since the CRISPR-dCas9 complex itself is fluorescent and remains bound during imaging. CRISPR-bind can be added directly to a sample that has already been labeled using DLS or NLRS, thus overlaying additional information onto the same molecules. Using the dCas9 protein assembled with custom target crRNA and fluorescently labeled tracrRNA, we demonstrate rapid labeling of repetitive DUF1220 elements. We also combine NLRS-based whole genome mapping with CRISPR-bind labeling targeting Alu loci. This rapid, convenient, non-damaging, and cost-effective technology is a valuable tool for custom labeling of any CRISPR-Cas9 amenable target sequence.

genomics↗

bioRxiv2017

OMSV enables accurate and comprehensive identification of large structural variations from nanochannel-based single-molecule optical maps

Human genomes contain structural variations (SVs) that are associated with various phenotypic variations and diseases. SV detection by sequencing is incomplete due to limited read length. Nanochannel-based optical mapping (OM) allows direct observation of SVs up to hundreds of kilo-bases in size on individual DNA molecules, making it a promising alternative technology for identifying large SVs. SV detection from optical maps is non-trivial due to complex types of error present in OM data, and no existing methods can simultaneously handle all these complex errors and the wide spectrum of SV types. Here we present a novel method, OMSV, for accurate and comprehensive identification of SVs from optical maps. OMSV detects both homozygous and heterozygous SVs, SVs of various types and sizes, and SVs with and without creating/destroying restriction sites. In an extensive series of tests based on real and simulated data, OMSV achieved both high sensitivity and specificity, with clear performance gains over the latest existing method. Applying OMSV to a human cell line, we identified hundreds of SVs >2kbp, with 65% of them missed by sequencing-based callers. Independent experimental validations confirmed the high accuracy of these SVs. We also demonstrate how OMSV can incorporate sequencing data to determine precise SV break points and novel sequences in the SVs not contained in the reference. We provide OMSV as open-source software to facilitate systematic studies of large SVs.

bioinformatics↗

bioRxiv2017

Rapid Automated Large Structural Variation Detection in a Diploid Genome by NanoChannel Based Next-Generation Mapping

The human genome is diploid with one haploid genome inherited from the maternal and one from the paternal lineage. Within each haploid genome, large structural variants such as deletions, duplications, inversions, and translocations are extensively present and many are known to affect biological functions and cause disease. The ultimate goal is to resolve these large complex structural variants (SVs) and place them in the correct haploid genome with correct location, orientation, and copy number. Current methods such as karyotyping, chromosomal microarray (CMA), PCR-based tests, and next-generation sequencing fail to reach this goal either due to limited resolution, low throughput, or short read length.\n\nBionano Genomics next-generation mapping (NGM) offers a high-throughput, genome-wide method able to detect SVs of one kilobase pairs (kbp) and up. By imaging extremely long genomic molecules of up to megabases in size, the structure and copy number of complex regions of the g ...

genomics↗