Biology subjects

Dillon, L. W.

Publications and source records attributed to Dillon, L. W..

4 recordsLinked to original sources

bioRxiv2025

Single Cell Spatial Transcriptomics Reveals Immunotherapy-Driven Bone Marrow Niche Remodeling in AML

Given the successful graft-versus-leukemia cell treatment effect observed with allogeneic hematopoietic stem cell transplant for patients with refractory or relapsed acute myeloid leukemia, immunotherapies have also been investigated in the nontransplant setting. Here, we use a multi-omic approach to investigate spatiotemporal interactions in the bone marrow niche between leukemia cells and immune cells in patients with refractory or relapsed acute myeloid leukemia treated with a combination of the immune checkpoint inhibitor pembrolizumab and hypomethylating agent decitabine. We derived precise segmentation data by extensively training nuclear and membrane cell segmentation models, which enabled accurate transcript assignment and deep learning-feature-based image analysis. To overcome read-depth limitations, we integrated the single-cell RNA sequencing data with single-cell-resolution spatial transcriptomic data from the same sample. Quantifying cell-cell distances between cell edges rather than cell centroids allowed us to conduct a more accurate downstream analysis of the tumor microenvironment, revealing that multiple cell types of interest had global enrichment or local enrichment proximal to leukemia cells after pembrolizumab treatment, which could be associated with their clinical responses. Furthermore, ligand-receptor analysis indicated a potential increase in TWEAK signaling between leukemia cells and immune cells after pembrolizumab treatment. HighlightsO_LISpatial transcriptomic analysis of R-AML bone marrow niches provides detailed information about intercellular interactions in the tumor microenvironment. C_LIO_LIImmunotherapy shifts the cell composition of the leukemia neighborhood. C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=199 SRC="FIGDIR/small/634753v1_ufig1.gif" ALT="Figure 1"> View larger version (93K): org.highwire.dtl.DTLVardef@955273org.highwire.dtl.DTLVardef@158234corg.highwire.dtl.DTLVardef@175837borg.highwire.dtl.DTLVardef@b0a38_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2022

Prediction of HLA genotypes from single-cell transcriptome data

The human leukocyte antigen (HLA) locus plays a central role in adaptive immune function and has significant clinical implications for tissue transplant compatibility and allelic disease associations. Studies using bulk-cell RNA sequencing have demonstrated that HLA transcription may be regulated in an allele-specific manner and single-cell RNA sequencing (scRNA-seq) has the potential to better characterize these expression patterns. However, quantification of allele-specific expression (ASE) for HLA loci requires sample-specific reference genotyping due to extensive polymorphism. While genotype prediction from bulk RNA sequencing is well described, the feasibility of predicting HLA genotypes directly from single-cell data is unknown. Here we evaluate and expand upon several computational HLA genotyping tools by comparing predictions from human single-cell data to gold-standard, molecular genotyping. The highest 2-field accuracy averaged across all loci was 76% by arcasHLA and increased to 86% using a composite model of multiple genotyping tools. We also developed a highly accurate model (AUC 0.93) for predicting HLA-DRB345 copy number in order to improve genotyping accuracy of the HLA-DRB locus. Genotyping accuracy improved with read depth and was reproducible at repeat sampling. Using a metanalytic approach, we also show that HLA genotypes from PHLAT and OptiType can generate ASE ratios that are highly correlated (R2 = 0.8 and 0.94, respectively) with those derived from gold-standard genotyping. Highlights- Benchmarking of HLA genotype prediction accuracy from 5- and 3-based single-cell RNA-seq data compared to molecular HLA genotyping - Quantification of transcript coverage by 5- and 3-based single cell sequencing methods - Accurate prediction of complex HLA-DRB345 copy numbers using supervised learning - Balancing accuracy and performance through composite HLA genotyping - Meta-analytic approach to summarizing allele-specific expression of HLA genotypes at single-cell level

bioinformatics↗

bioRxiv2018

Molecular measurable residual disease testing of blood during AML cytotoxic therapy for early prediction of clinical response

AbstractO_ST_ABSPURPOSEC_ST_ABSMeasurable residual disease (MRD) testing after initial chemotherapy treatment can predict relapse and survival in AML. However, it has not been established if repeat molecular or genetic testing during chemotherapy can offer information regarding the chemotherapy sensitivity of the leukemic clone.\n\nPATIENTS AND METHODSBlood from 45 adult AML patients at day 1 and 4 of induction (n = 35) or salvage (n = 10) cytotoxic chemotherapy was collected for both quantitative real-time PCR (qPCR) assessment (WT1) and next generation sequencing (NGS, >500x depth) of 49 gene regions recurrently mutated in MDS/AML.\n\nRESULTSThe median age was 62 (23-78); 42% achieved a complete response. WT1 was overexpressed in most patients tested but was uninformative for very early MRD assessment. A median of 4 non-synonymous variants (range 0-7) were detected by DNA sequencing of blood on day 1 of therapy (median VAF: 29%). Only two patients had no variants detectable. All mutations remained detectable in blood on day 4 of intensive chemotherapy and remarkably the ratio of mutated to wild-type sequence was often maintained. This phenomenon was not limited to variants in DNMT3A, TET2 and ASXL1. The kinetics of NPM1 and TP53 variant burden early during chemotherapy appeared to be exceptions and exhibited consistent trends in this cohort.\n\nCONCLUSIONSMolecular testing of blood on day 4 of chemotherapy is not predictive of clinical response in AML. The observed stability in variant allele frequency suggests that cytotoxic therapy may have a limited therapeutic index for clones circulating in blood containing these mutations. Further validation is required to confirm the utility of monitoring NPM1 and TP53 kinetics in blood during cytotoxic therapy.

cancer biology↗

bioRxiv2018

Human Bone Marrow Assessment by Single Cell RNA Sequencing, Mass Cytometry and Flow Cytometry

New techniques for single-cell analysis have led to insights into hematopoiesis and the immune system, but the ability of these techniques to cross-validate and reproducibly identify the biological variation in diverse human samples is currently unproven. We therefore performed a comprehensive assessment of human bone marrow cells using both single-cell RNA sequencing and multiparameter flow cytometry from twenty healthy adult human donors across a broad age range. These data characterize variation between healthy donors as well as age-associated changes in cell population frequencies. Direct comparison of techniques revealed discrepancy in the quantification of T lymphocyte and natural killer cell populations. Orthogonal validation of immunophenotyping using mass cytometry demonstrated good correlation with flow cytometry. Technical replicates using single-cell RNA sequencing matched robustly, while biological replicates showed variation. Given the increasing use of single-cell technologies in translational research, this resource serves as an important reference dataset and highlights opportunities for further refinement.

immunology↗