Biology subjects

Samusik, N.

Publications and source records attributed to Samusik, N..

6 recordsLinked to original sources

bioRxiv2022

Identification of cell types in multiplexed in situ images by combining protein expression and spatial information using CELESTA reveals novel spatial biology

Advances in multiplexed in situ imaging are revealing important insights in spatial biology. However, cell type identification remains a major challenge in imaging analysis, with most existing methods involving substantial manual assessment and subjective decisions for thousands of cells. We propose a novel machine learning algorithm, CELESTA, which uses both cells protein expression and spatial information to identify cell type of individual cells. We demonstrate the performance of CELESTA on multiplexed immunofluorescence in situ images of colorectal cancer and head and neck cancer. Using the cell types identified by CELESTA, we identify tissue architecture associated with lymph node metastasis in HNSCC, which we validate in an independent cohort. By coupling our in situ spatial analysis with single-cell RNA-sequencing data on proximal sections of the same tissue specimens, we identify and validate cell-cell crosstalk associated with lymph node metastasis, demonstrating the power of spatial biology to reveal clinically-relevant cellular interactions.

bioinformatics↗

bioRxiv2019

Coordinated cellular neighborhoods orchestrate antitumoral immunity at the colorectal cancer invasive front

Antitumoral immunity requires organized, spatially nuanced interactions between components of the immune tumor microenvironment (iTME). Understanding this coordinated behavior in effective versus ineffective tumor control will advance immunotherapies. We optimized CO-Detection by indEXing (CODEX) for para ffin-em bedded tissue microarrays, enabling profiling of 140 tissue regions from 35 advanced-stage colorectal cancer (CRC) patients with 56 protein markers simultaneously. We identified nine conserved, distinct cellular neighborhoods (CNs)-a collection of components characteristic of the CRC iTME. Enrichment of PD-1+CD4+ T cells only within a granulocyte CN positively correlated with survival in a high-risk patient subset. Coupling of tumor and immune CNs, fragmentation of T cell and macrophage CNs, and disruption of inter-CN communication was associated with inferior outcomes. This study provides a framework for interrogating complex biological processes, such as antitumoral immunity, demonstrating an example of how tumors can disrupt imm une functionality through interference in the concerted action of cells and spatial domains.

immunology↗

bioRxiv2019

Profiling Myelodysplastic Syndromes by Mass Cytometry Demonstrates Abnormal Progenitor Cell Phenotype and Differentiation

PurposeWe sought to enhance the cytometric analysis of MDS by performing a pilot study of a single cell mass cytometry (MCM) assay to more comprehensively analyze patterns of surface marker expression in patients with MDS.\n\nExperimental DesignTwenty-three MDS and five healthy donor bone marrow samples were studied using a 34-parameter mass cytometry panel utilizing barcoding and internal reference standards. The resulting data were analyzed by both traditional gating and high-dimensional clustering.\n\nResultsThis high-dimensional assay provided three major benefits relative to traditional cytometry approaches: First, MCM enabled detection of aberrant surface maker at high resolution, detecting aberrancies in 27/31 surface markers, encompassing almost every previously reported MDS surface marker aberrancy. Additionally, three previously unrecognized aberrancies in MDS were detected in multiple samples at least one developmental stage: increased CD321 and CD99; and decreased CD47. Second, analysis of the stem and progenitor cell compartment (HSPCs), demonstrated aberrant expression in 21 of the 23 MDS samples, which were not detected in three samples from patients with idiopathic cytopenia of undetermined significance (ICUS). These immunophenotypically abnormal HSPCs were also the single most significant distinguishing feature between clinical risk groups. Third, unsupervised clustering of high-parameter MCM data allowed identification of abnormal differentiation patterns associated with immunophenotypically aberrant myeloid cells similar to myeloid derived suppressor cells.\n\nConclusionsThese results demonstrate that high-parameter cytometry methods that enable simultaneous analysis of all bone marrow cell types could enhance the diagnostic utility of immunophenotypic analysis in MDS.\n\nStatement of SignificanceO_LIHigh-dimensional mass cytometry enables high-resolution characterization of abnormal maker expression and myeloid development in MDS.\nC_LIO_LIThis technology could enhance MDS diagnosis and therapeutic monitoring and merits further research.\nC_LI\n\nStatement Translational RelevanceIn spite of several studies suggesting the utility of flow cytometry in the diagnosis of myelodysoplastic syndrome (MDS), this technique has not been widely adopted. We sought to enhance the utility of cytometry in MDS by performing the first high-dimensional mass cytometry characterization of a cohort of MDS patients. High-dimensional mass cytometry allowed all bone marrow cell populations to be simultaneously analyzed enabling high-resolution characterization of abnormal maker expression and myeloid development in MDS. This approach could identify almost all previously identified aberrant surface marker expression patterns in MDS while simultaneously enabling analysis by unsupervised clustering. Additionally, this mass cytometry analysis approach enabled the modeling of abnormal differentiation in MDS. This technology could enhance MDS diagnosis and therapeutic monitoring and merits further research.

cancer biology↗

bioRxiv2018

Continuous visualization of differences between biological conditions in single-cell data

In high-dimensional single cell data, comparing changes in functional markers between conditions is typically done across manual or algorithm-derived partitions based on population-defining markers. Visualizations of these partitions is commonly done on low-dimensional embeddings (eg. t-SNE), colored by per-partition changes. Here, we provide an analysis and visualization tool that performs these comparisons across overlapping k-nearest neighbor (KNN) groupings. This allows one to color low-dimensional embeddings by marker changes without hard boundaries imposed by partitioning. We devised an objective optimization of k based on minimizing functional marker KNN imputation error. Proof-of-concept work visualized the exact location of an IL-7 responsive subset in a B cell developmental trajectory on a t-SNE map independent of clustering. Per-condition cell frequency analysis revealed that KNN is sensitive to detecting artifacts due to marker shift, and therefore can also be valuable in a quality control pipeline. Overall, we found that KNN groupings lead to useful multiple condition visualizations and efficiently extract a large amount of information from mass cytometry data. Our software is publicly available through the Bioconductor package Sconify.

systems biology↗

bioRxiv2017

Deep profiling of mouse splenic architecture with CODEX multiplexed imaging.

A highly multiplexed cytometric imaging approach, termed CO-Detection by indEXing (CODEX), is used here to create multiplexed datasets of normal and lupus (MRL/lpr) murine spleens. CODEX iteratively visualizes antibody binding events using DNA barcodes, fluorescent dNTP analogs, and an insitu polymerization-based indexing procedure. An algorithmic pipeline for single-cell antigen quantification in tightly packed tissues was developed and used to overlay well-known morphological features with de novo characterization of lymphoid tissue architecture at a single-cell and cellular neighborhood levels. We observed an unexpected, profound impact of the cellular neighborhood on the expression of protein receptors on immune cells. By comparing normal murine spleen to spleens from animals with systemic autoimmune disease (MRL/lpr), extensive and previously uncharacterized splenic cell interaction dynamics in the healthy versus diseased state was observed. The fidelity of multiplexed spatial cytometry demonstrated here allows for quantitative systemic characterization of tissue architecture in normal and clinically aberrant samples.

systems biology↗

bioRxiv2017

Scalable Multi-Sample Single-Cell Data Analysis by Partition-Assisted Clustering and Multiple Alignments of Networks

Mass cytometry (CyTOF) has greatly expanded the capability of cytometry. It is now easy to generate multiple CyTOF samples in a single study, with each sample containing single-cell measurement on 50 markers for more than hundreds of thousands of cells. Current methods do not adequately address the issues concerning combining multiple samples for subpopulation discovery, and these issues can be quickly and dramatically amplified with increasing number of samples. To overcome this limitation, we developed Partition-Assisted Clustering and Multiple Alignments of Networks (PAC-MAN) for the fast automatic identification of cell populations in CyTOF data closely matching that of expert manual-discovery, and for alignments between subpopulations across samples to define dataset-level cellular states. PAC-MAN is computationally efficient, allowing the management of very large CyTOF datasets, which are increasingly common in clinical studies and cancer studies that monitor various tissue samples for each subject.\n\nAuthor SummaryRecently, the cytometry field has experienced rapid advancement in the development of mass cytometry (CyTOF). CyTOF enables a significant increase in the ability to monitor 50 or more cellular markers for millions of cells at the single-cell level. Initial studies with CyTOF focused on few samples, in which expert manual discovery of cell types were acceptable. As the technology matures, it is now feasible to collect more samples, which enables systematic studies of cell types across multiple samples. However, the statistical and computational issues surrounding multi-sample analysis have not been previously examined in detail. Furthermore, it was not clear how the data analysis could be scaled for hundreds of samples, such as those in clinical studies. In this work, we present a scalable analysis pipeline that is grounded in strong statistical foundation. Partition-Assisted Clustering (PAC) offers fast and accurate clustering and Multiple Alignments of Networks (MAN) utilizes network structures learned from each homogeneous cluster to organize the data into data-set level clusters. PAC-MAN thus enables the analysis of a large CyTOF dataset that was previously too large to be analyzed systematically; this pipeline can be extended to the analysis of similarly large or larger datasets.

bioinformatics↗