Biology subjects

McKinley, E. T.

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

7 recordsLinked to original sources

bioRxiv2023

GammaGateR: semi-automated marker gating for single-cell multiplexed imaging

MotivationMultiplexed immunofluorescence (mIF) is an emerging assay for multichannel protein imaging that can decipher cell-level spatial features in tissues. However, existing automated cell phenotyping methods, such as clustering, face challenges in achieving consistency across experiments and often require subjective evaluation. As a result, mIF analyses often revert to marker gating based on manual thresholding of raw imaging data. ResultsTo address the need for an evaluable semi-automated algorithm, we developed GammaGateR, an R package for interactive marker gating designed specifically for segmented cell-level data from mIF images. Based on a novel closed-form gamma mixture model, GammaGateR provides estimates of marker-positive cell proportions and soft clustering of marker-positive cells. The model incorporates user-specified constraints that provide a consistent but slide-specific model fit. We compared GammaGateR against the newest unsupervised approach for annotating mIF data, employing two colon datasets and one ovarian cancer dataset for the evaluation. We showed that GammaGateR produces highly similar results to a silver standard established through manual annotation. Furthermore, we demonstrated its effectiveness in identifying biological signals, achieved by mapping known spatial interactions between CD68 and MUC5AC cells in the colon and by accurately predicting survival in ovarian cancer patients using the phenotype probabilities as input for machine learning methods. GammaGateR is a highly efficient tool that can improve the replicability of marker gating results, while reducing the time of manual segmentation. Availability and ImplementationThe R package is available at https://github.com/JiangmeiRubyXiong/GammaGateR. ContactPlease address correspondence to jiangmei.xiong@vanderbilt.edu and simon.vandekar@vumc.org. Key PointsO_LIGammaGateR is the first semi-automated marker gating tool for mIF image, and it will help to diminish the inconsistency with manual marker gating. C_LIO_LIWith novel cfGMM, GammaGateR can fit flexibly across slides with different distributions and incorporate biology priors. C_LIO_LIGammaGateR is proven to reveal credible prognostic information, and it can quantify known findings in tumor cell populations. C_LI

bioinformatics↗

bioRxiv2023

Molecular cartography uncovers evolutionary and microenvironmental dynamics in sporadic colorectal tumors

Colorectal cancer exhibits dynamic cellular and genetic heterogeneity during progression from precursor lesions toward malignancy. Leveraging spatial molecular information to construct a phylogeographic map of tumor evolution can reveal individualized growth trajectories with diagnostic and therapeutic potential. Integrative analysis of spatial multi-omic data from 31 colorectal specimens revealed simultaneous microenvironmental and clonal alterations as a function of progression. Copy number variation served to re-stratify microsatellite stable and unstable tumors into chromosomally unstable (CIN+) and hypermutated (HM) classes. Phylogeographical maps classified tumors by their evolutionary dynamics, and clonal regions were placed along a global pseudotemporal progression trajectory. Cell-state discovery from a single-cell cohort revealed recurring epithelial gene signatures and infiltrating immune states in spatial transcriptomics. Charting these states along progression pseudotime, we observed a transition to immune exclusion in CIN+ tumors as characterized by a novel gene expression signature comprised of DDR1, TGFBI, PAK4, and DPEP1. We demonstrated how these genes and their protein products are key regulators of extracellular matrix components, are associated with lower cytotoxic immune infiltration, and show prognostic value in external cohorts. Through high-dimensional data integration, this atlas provides insights into co-evolution of tumors and their microenvironments, serving as a resource for stratification and targeted treatment of CRC.

systems biology↗

bioRxiv2023

Consensus tissue domain detection in spatial -omics data using MILWRM

Spatially resolved molecular assays provide high dimensional genetic, transcriptomic, proteomic, and epigenetic information in situ and at various resolutions. Pairing these data across modalities with histological features enables powerful studies of tissue pathology in the context of an intact microenvironment and tissue structure. Increasing dimensions across molecular analytes and samples require new data science approaches to functionally annotate spatially resolved molecular data. A specific challenge is data-driven cross-sample domain detection that allows for analysis within and between consensus tissue compartments across high volumes of multiplex datasets stemming from tissue atlasing efforts. Here, we present MILWRM - multiplex image labeling with regional morphology - a Python package for rapid, multi-scale tissue domain detection and annotation. We demonstrate MILWRMs utility in identifying histologically distinct compartments in human colonic polyps and mouse brain slices through spatially-informed clustering in two different spatial data modalities. Additionally, we used tissue domains detected in human colonic polyps to elucidate molecular distinction between polyp subtypes. We also explored the ability of MILWRM to identify anatomical regions of mouse brain and their respective distinct molecular profiles.

systems biology↗

bioRxiv2021

Quantifying and correcting slide-to-slide variation in multiplexed immunofluorescence images

MotivationThe multiplexed imaging domain is a nascent single-cell analysis field with a complex data structure susceptible to technical variability that disrupts inference. These in situ methods are valuable in understanding cell-cell interactions, but few standardized processing steps or normalization techniques of multiplexed imaging data are available. ResultsWe implement and compare data transformations and normalization algorithms in multiplexed imaging data. Our methods adapt the ComBat and functional data registration methods to remove slide effects in this domain, and we present an evaluation framework to compare the proposed approaches. We present clear slide-to-slide variation in the raw, unadjusted data, and show that many of the proposed normalization methods reduce this variation while preserving and improving the biological signal. Further, we find that dividing this data by its slide mean, and the functional data registration methods, perform the best under our proposed evaluation framework. In summary, this approach provides a foundation for better data quality and evaluation criteria in the multiplexed domain. Availability and ImplementationSource code is provided at https://github.com/statimagcoll/MultiplexedNormalization. Contactcoleman.r.harris@vanderbilt.edu Supplementary informationSupplementary information is available online.

bioinformatics↗

bioRxiv2021

MCMICRO: A scalable, modular image-processing pipeline for multiplexed tissue imaging

Highly multiplexed tissue imaging makes molecular analysis of single cells possible in a preserved spatial context. However, reproducible analysis of the underlying data poses a substantial computational challenge. Here we describe a modular and open-source computational pipeline (MCMICRO) for performing the sequential steps needed to transform large, multi-channel whole slide images into single-cell data. We demonstrate use of MCMICRO on images of different tissues and tumors acquired using multiple imaging platforms, thereby providing a solid foundation for the continued development of tissue imaging software.

bioinformatics↗

bioRxiv2021

Human colorectal pre-cancer atlas identifies distinct molecular programs underlying two major subclasses of pre-malignant tumors

Most colorectal cancers (CRCs) develop from either adenomas (ADs) or sessile serrated lesions (SSLs). The origins and molecular landscapes of these histologically distinct pre-cancerous polyps remain incompletely understood. Here, we present an atlas at single-cell resolution of sporadic conventional tubular/tubulovillous ADs, SSLs, hyperplastic polyps (HPs), microsatellite stable (MSS) and unstable (MSI-H) CRC, and normal colonic mucosa. Using single-cell transcriptomics and multiplex imaging, we studied 69 datasets from 33 participants. We also examined separate sets of 66 and 274 polyps for RNA and targeted gene sequencing, respectively. We performed multiplex imaging on a tissue microarray of 14 ADs and 15 CRCs, and we integrated pre-cancer polyp data with published single-cell and The Cancer Genome Atlas (TCGA) bulk CRC data to establish potential polyp-cancer relationships. Striking differences were observed between ADs and SSLs that extended to MSS and MSI-H CRCs, respectively, reflecting their distinct origins and trajectories. ADs arose from WNT pathway dysregulation in stem cells, which aberrantly expanded and expressed a Hippo and ASCL2 regenerative program. In marked contrast, SSLs were depleted of stem cell-like populations and instead exhibited a program of gastric metaplasia in the setting of elevated cytotoxic inflammation. Using subtype-specific gene regulatory networks and shared genetic variant analysis, we implicated serrated polyps, including some HPs conventionally considered benign, as arising from a metaplastic program in committed absorptive cells. ADs and SSLs displayed distinct patterns of immune cell infiltration that may influence their natural history. Our multi-omic atlas provides novel insights into the malignant potential of colorectal polyps and serves as a framework for precision surveillance and prevention of sporadic CRC.

cancer biology↗

bioRxiv2019

Machine and deep learning single-cell segmentation and quantification of multi-dimensional tissue images

Increasingly, highly multiplexed in situ tissue imaging methods are used to profile protein expression at the single-cell level. However, a critical limitation is a lack of robust cell segmentation tools applicable for sections of tissues with a complex architecture and multiple cell types. Using human colorectal adenomas, we present a pipeline for cell segmentation and quantification that utilizes machine learning-based pixel classification to define cellular compartments, a novel method for extending incomplete cell membranes, quantification of antibody staining, and a deep learning-based cell shape descriptor. We envision that this method can be broadly applied to different imaging platforms and tissue types.

bioinformatics↗