MIRIAM: A machine and deep learning single-cell segmentation and quantification pipeline for multi-dimensional tissue images

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

MOTIVATION

Multiplexed 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.

RESULTS

To 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 IMPLEMENTATION

The R package is available at https://github.com/JiangmeiRubyXiong/GammaGateR.

Recent Advancements in Subcellular Proteomics: Growing Impact of Organellar Protein Niches on the Understanding of Cell Biology

The mammalian cell is a complex entity, with membrane-bound and membrane-less organelles playing vital roles in regulating cellular homeostasis. Organellar protein niches drive discrete biological processes and cell functions, thus maintaining cell equilibrium. Cellular processes such as signaling, growth, proliferation, motility, and programmed cell death require dynamic protein movements between cell compartments. Aberrant protein localization is associated with a wide range of diseases. Therefore, analyzing the subcellular proteome of the cell can provide a comprehensive overview of cellular biology. With recent advancements in mass spectrometry, imaging technology, computational tools, and deep machine learning algorithms, studies pertaining to subcellular protein localization and their dynamic distributions are gaining momentum. These studies reveal changing interaction networks because of "moonlighting proteins" and serve as a discovery tool for disease network mechanisms. Consequently, this review aims to provide a comprehensive repository for recent advancements in subcellular proteomics subcontexting methods, challenges, and future perspectives for method developers. In summary, subcellular proteomics is crucial to the understanding of the fundamental cellular mechanisms and the associated diseases.

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. Analysis of spatial multi-omic data from 31 human colorectal specimens enabled phylogeographic mapping of tumor evolution that revealed individualized progression trajectories and accompanying microenvironmental and clonal alterations. Phylogeographic mapping ordered genetic events, classified tumors by their evolutionary dynamics, and placed clonal regions along global pseudotemporal progression trajectories encompassing the chromosomal instability (CIN+) and hypermutated (HM) pathways. Integrated single-cell and spatial transcriptomic data revealed recurring epithelial programs and infiltrating immune states along progression pseudotime. We discovered an immune exclusion signature (IEX), consisting of extracellular matrix regulators DDR1, TGFBI, PAK4, and DPEP1, that charts with CIN+ tumor progression, is associated with reduced cytotoxic cell infiltration, and shows prognostic value in independent cohorts. This spatial multi-omic atlas provides insights into colorectal tumor-microenvironment co-evolution, serving as a resource for stratification and targeted treatments.

A novel process for H&E, immunofluorescence, and imaging mass cytometry on a single slide with a concise analytics pipeline

Imaging mass cytometry (IMC) is a powerful spatial technology that utilizes cytometry time of flight to acquire multiplexed image datasets with up to 40 markers, via metal-tagged antibodies. Recent advances in IMC have led to the inclusion of RNAScope probes and multiple new analysis pipelines have led to faster analyses and better results. However, IMC still suffers from lower resolution (1 μm2 pixels) and relatively small regions of interest (ROIs) (<2 mm2 ) compared to other, light-based microscope technologies. Capturing higher-resolution images on serial sections causes great difficulty when attempting to align cells and structures across serial sections, especially when observing smaller cell types and structures. Therefore, we demonstrate the combination of H&E and multiplex immunofluorescence imaging, for much higher resolution of the structural and cellular compartments found throughout the entire tissue section, with the high-dimensionality of IMC for specific ROIs on a single slide. Additionally, we demonstrate a simple and effective open-source cell segmentation and IMC analysis pipeline with previously published and freely available software.

An end-to-end workflow for multiplexed image processing and analysis

Multiplexed imaging enables the simultaneous spatial profiling of dozens of biological molecules in tissues at single-cell resolution. Extracting biologically relevant information, such as the spatial distribution of cell phenotypes from multiplexed tissue imaging data, involves a number of computational tasks, including image segmentation, feature extraction and spatially resolved single-cell analysis. Here, we present an end-to-end workflow for multiplexed tissue image processing and analysis that integrates previously developed computational tools to enable these tasks in a user-friendly and customizable fashion. For data quality assessment, we highlight the utility of napari-imc for interactively inspecting raw imaging data and the cytomapper R/Bioconductor package for image visualization in R. Raw data preprocessing, image segmentation and feature extraction are performed using the steinbock toolkit. We showcase two alternative approaches for segmenting cells on the basis of supervised pixel classification and pretrained deep learning models. The extracted single-cell data are then read, processed and analyzed in R. The protocol describes the use of community-established data containers, facilitating the application of R/Bioconductor packages for dimensionality reduction, single-cell visualization and phenotyping. We provide instructions for performing spatially resolved single-cell analysis, including community analysis, cellular neighborhood detection and cell-cell interaction testing using the imcRtools R/Bioconductor package. The workflow has been previously applied to imaging mass cytometry data, but can be easily adapted to other highly multiplexed imaging technologies. This protocol can be implemented by researchers with basic bioinformatics training, and the analysis of the provided dataset can be completed within 5-6 h. An extended version is available at https://bodenmillergroup.github.io/IMCDataAnalysis/ .

A Specialized Epithelial Cell Type Regulating Mucosal Immunity and Driving Human Crohn's Disease

Crohn's disease (CD) is a complex chronic inflammatory disorder that may affect any part of gastrointestinal tract with extra-intestinal manifestations and associated immune dysregulation. To characterize heterogeneity in CD, we profiled single-cell transcriptomics of 170 samples from 65 CD patients and 18 non-inflammatory bowel disease (IBD) controls in both the terminal ileum (TI) and ascending colon (AC). Analysis of 202,359 cells identified a novel epithelial cell type in both TI and AC, featuring high expression of LCN2, NOS2, and DUOX2, and thus is named LND. LND cells, confirmed by high-resolution in-situ RNA imaging, were rarely found in non-IBD controls, but expanded significantly in active CD. Compared to other epithelial cells, genes defining LND cells were enriched in antimicrobial response and immunoregulation. Moreover, multiplexed protein imaging demonstrated that LND cell abundance was associated with immune infiltration. Cross-talk between LND and immune cells was explored by ligand-receptor interactions and further evidenced by their spatial colocalization. LND cells showed significant enrichment of expression specificity of IBD/CD susceptibility genes, revealing its role in immunopathogenesis of CD. Investigating lineage relationships of epithelial cells detected two LND cell subpopulations with different origins and developmental potential, early and late LND. The ratio of the late to early LND cells was related to anti-TNF response. These findings emphasize the pathogenic role of the specialized LND cell type in both Crohn's ileitis and Crohn's colitis and identify novel biomarkers associated with disease activity and treatment response.

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

Motivation

Multiplexed 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.

Results

To 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 Implementation

The R package is available at https://github.com/JiangmeiRubyXiong/GammaGateR.

Deep learning exploration of single-cell and spatially resolved cancer transcriptomics to unravel tumour heterogeneity

Tumour heterogeneity is one of the critical confounding aspects in decoding tumour growth. Malignant cells display variations in their gene transcription profiles and mutation spectra even when originating from a single progenitor cell. Single-cell and spatial transcriptomics sequencing have recently emerged as key technologies for unravelling tumour heterogeneity. Single-cell sequencing promotes individual cell-type identification through transcriptome-wide gene expression measurements of each cell. Spatial transcriptomics facilitates identification of cell-cell interactions and the structural organization of heterogeneous cells within a tumour tissue through associating spatial RNA abundance of cells at distinct spots in the tissue section. However, extracting features and analyzing single-cell and spatial transcriptomics data poses challenges. Single-cell transcriptome data is extremely noisy and its sparse nature and dropouts can lead to misinterpretation of gene expression and the misclassification of cell types. Deep learning predictive power can overcome data challenges, provide high-resolution analysis and enhance precision oncology applications that involve early cancer prognosis, diagnosis, patient survival estimation and anti-cancer therapy planning. In this paper, we provide a background to and review of the recent progress of deep learning frameworks to investigate tumour heterogeneity using both single-cell and spatial transcriptomics data types.

Unleashing the potential of AI for pathology: challenges and recommendations

Computational pathology is currently witnessing a surge in the development of AI techniques, offering promise for achieving breakthroughs and significantly impacting the practices of pathology and oncology. These AI methods bring with them the potential to revolutionize diagnostic pipelines as well as treatment planning and overall patient care. Numerous peer-reviewed studies reporting remarkable performance across diverse tasks serve as a testimony to the potential of AI in the field. However, widespread adoption of these methods in clinical and pre-clinical settings still remains a challenge. In this review article, we present a detailed analysis of the major obstacles encountered during the development of effective models and their deployment in practice. We aim to provide readers with an overview of the latest developments, assist them with insights into identifying some specific challenges that may require resolution, and suggest recommendations and potential future research directions. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

A comparison of machine learning approaches for the quantification of microglial cells in the brain of mice, rats and non-human primates

Microglial cells are brain-specific macrophages that swiftly react to disruptive events in the brain. Microglial activation leads to specific modifications, including proliferation, morphological changes, migration to the site of insult, and changes in gene expression profiles. A change in inflammatory status has been linked to many neurodegenerative diseases such as Parkinson's disease and Alzheimer's disease. For this reason, the investigation and quantification of microglial cells is essential for better understanding their role in disease progression as well as for evaluating the cytocompatibility of novel therapeutic approaches for such conditions. In the following study we implemented a machine learning-based approach for the fast and automatized quantification of microglial cells; this tool was compared with manual quantification (ground truth), and with alternative free-ware such as the threshold-based ImageJ and the machine learning-based Ilastik. We first trained the algorithms on brain tissue obtained from rats and non-human primate immunohistochemically labelled for microglia. Subsequently we validated the accuracy of the trained algorithms in a preclinical rodent model of Parkinson's disease and demonstrated the robustness of the algorithms on tissue obtained from mice, as well as from images provided by three collaborating laboratories. Our results indicate that machine learning algorithms can detect and quantify microglial cells in all the three mammalian species in a precise manner, equipotent to the one observed following manual counting. Using this tool, we were able to detect and quantify small changes between the hemispheres, suggesting the power and reliability of the algorithm. Such a tool will be very useful for investigation of microglial response in disease development, as well as in the investigation of compatible novel therapeutics targeting the brain. As all network weights and labelled training data are made available, together with our step-by-step user guide, we anticipate that many laboratories will implement machine learning-based quantification of microglial cells in their research.

Influence of Cell-type Ratio on Spatially Resolved Single-cell Transcriptomes using the Tangram Algorithm: Based on Implementation on Single-Cell and MxIF Data

The Tangram algorithm is a benchmarking method of aligning single-cell (sc/snRNA-seq) data to various forms of spatial data collected from the same region. With this data alignment, the annotation of the single-cell data can be projected to spatial data. However, the cell composition (cell-type ratio) of the single-cell data and spatial data might be different because of heterogeneous cell distribution. Whether the Tangram algorithm can be adapted when the two data have different cell-type ratios has not been discussed in previous works. In our practical application that maps the cell-type classification results of single-cell data to the Multiplex immunofluorescence (MxIF) spatial data, cell-type ratios were different, though they were sampled from adjacent areas. In this work, both simulation and empirical validation were conducted to quantitatively explore the impact of the mismatched cell-type ratio on the Tangram mapping in different situations. Results show that the cell-type difference has a negative influence on classification accuracy.

Topological-Preserving Membrane Skeleton Segmentation in Multiplex Immunofluorescence Imaging

Multiplex immunofluorescence (MxIF) is an emerging imaging technology whose downstream molecular analytics highly rely upon the effectiveness of cell segmentation. In practice, multiple membrane markers (e.g., NaKATPase, PanCK and β-catenin) are employed to stain membranes for different cell types, so as to achieve a more comprehensive cell segmentation since no single marker fits all cell types. However, prevalent watershed-based image processing might yield inferior capability for modeling complicated relationships between markers. For example, some markers can be misleading due to questionable stain quality. In this paper, we propose a deep learning based membrane segmentation method to aggregate complementary information that is uniquely provided by large scale MxIF markers. We aim to segment tubular membrane structure in MxIF data using global (membrane markers z-stack projection image) and local (separate individual markers) information to maximize topology preservation with deep learning. Specifically, we investigate the feasibility of four SOTA 2D deep networks and four volumetric-based loss functions. We conducted a comprehensive ablation study to assess the sensitivity of the proposed method with various combinations of input channels. Beyond using adjusted rand index (ARI) as the evaluation metric, which was inspired by the clDice, we propose a novel volumetric metric that is specific for skeletal structure, denoted as c l D i c e S K E L . In total, 80 membrane MxIF images were manually traced for 5-fold cross-validation. Our model outperforms the baseline with a 20.2% and 41.3% increase in c l D i c e S K E L and ARI performance, which is significant (p<0.05) using the Wilcoxon signed rank test. Our work explores a promising direction for advancing MxIF imaging cell segmentation with deep learning membrane segmentation. Tools are available at https://github.com/MASILab/MxIF_Membrane_Segmentation.

YOUPI: Your powerful and intelligent tool for segmenting cells from imaging mass cytometry data

The recent emergence of imaging mass cytometry technology has led to the generation of an increasing amount of high-dimensional data and, with it, the need for suitable performant bioinformatics tools dedicated to specific multiparametric studies. The first and most important step in treating the acquired images is the ability to perform highly efficient cell segmentation for subsequent analyses. In this context, we developed YOUPI (Your Powerful and Intelligent tool) software. It combines advanced segmentation techniques based on deep learning algorithms with a friendly graphical user interface for non-bioinformatics users. In this article, we present the segmentation algorithm developed for YOUPI. We have set a benchmark with mathematics-based segmentation approaches to estimate its robustness in segmenting different tissue biopsies.

Statistical Analysis of Multiplex Immunofluorescence and Immunohistochemistry Imaging Data

Advances in multiplexed single-cell immunofluorescence (mIF) and multiplex immunohistochemistry (mIHC) imaging technologies have enabled the analysis of cell-to-cell spatial relationships that promise to revolutionize our understanding of tissue-based diseases and autoimmune disorders. Multiplex images are collected as multichannel TIFF files; then denoised, segmented to identify cells and nuclei, normalized across slides with protein markers to correct for batch effects, and phenotyped; and then tissue composition and spatial context at the cellular level are analyzed. This chapter discusses methods and software infrastructure for image processing and statistical analysis of mIF/mIHC data.

Application of Machine Learning in Spatial Proteomics

Spatial proteomics is an interdisciplinary field that investigates the localization and dynamics of proteins, and it has gained extensive attention in recent years, especially the subcellular proteomics. Numerous evidence indicate that the subcellular localization of proteins is associated with various cellular processes and disease progression. Mass spectrometry (MS)-based and imaging-based experimental approaches have been developed to acquire large-scale spatial proteomic data. To allow the reliable analysis of increasingly complex spatial proteomics data, machine learning (ML) methods have been widely used in both MS-based and imaging-based spatial proteomic data analysis pipelines. Here, we comprehensively survey the applications of ML in spatial proteomics from following aspects: (1) data resources for spatial proteome are comprehensively introduced; (2) the roles of different ML algorithms in data analysis pipelines are elaborated; (3) successful applications of spatial proteomics and several analytical tools integrating ML methods are presented; (4) challenges existing in modern ML-based spatial proteomics studies are discussed. This review provides guidelines for researchers seeking to apply ML methods to analyze spatial proteomic data and can facilitate insightful understanding of cell biology as well as the future research in medical and drug discovery communities.

Cancer-Associated Fibroblasts and Squamous Epithelial Cells Constitute a Unique Microenvironment in a Mouse Model of Inflammation-Induced Colon Cancer

The tumor microenvironment plays a key role in the pathogenesis of colorectal tumors and contains various cell types including epithelial, immune, and mesenchymal cells. Characterization of the interactions between these cell types is necessary for revealing the complex nature of tumors. In this study, we used single-cell RNA-seq (scRNA-seq) to compare the tumor microenvironments between a mouse model of sporadic colorectal adenoma (Lrig1^CreERT2/+;Apc^2lox14/+) and a mouse model of inflammation-driven colorectal cancer induced by azoxymethane and dextran sodium sulfate (AOM/DSS). While both models develop tumors in the distal colon, we found that the two tumor types have distinct microenvironments. AOM/DSS tumors have an increased abundance of two populations of cancer-associated fibroblasts (CAFs) compared with APC tumors, and we revealed their divergent spatial association with tumor cells using multiplex immunofluorescence (MxIF) imaging. We also identified a unique squamous cell population in AOM/DSS tumors, whose origins were distinct from anal squamous epithelial cells. These cells were in higher proportions upon administration of a chemotherapy regimen of 5-Fluorouracil/Irinotecan. We used computational inference algorithms to predict cell-cell communication mediated by ligand-receptor interactions and downstream pathway activation, and identified potential mechanistic connections between CAFs and tumor cells, as well as CAFs and squamous epithelial cells. This study provides important preclinical insight into the microenvironment of two distinct models of colorectal tumors and reveals unique roles for CAFs and squamous epithelial cells in the AOM/DSS model of inflammation-driven cancer.

mxnorm: An R Package to Normalize Multiplexed Imaging Data

Multiplexed imaging is an emerging single-cell assay that can be used to understand and analyze complex processes in tissue-based cancers, autoimmune disorders, and more. These imaging technologies, which include co-detection by indexing (CODEX), multiplexed ion beam imaging (MIBI), and multiplexed immunofluorescence imaging (MxIF), provide detailed information about spatial interactions between cells (Angelo et al., 2014; Gerdes et al., 2013; Goltsev et al., 2018). Multiplexed imaging experiments generate data across hundreds of slides and images, often resulting in terabytes of complex data to analyze through imaging analysis pipelines. Methods are rapidly developing to improve particular parts of the pipeline, including software packages in R and Python like spatialTime, imcRtools, MCMICR0, and Squidpy (Creed et al., 2021; Palla et al., 2021; Schapiro et al., 2021; Windhager et al., 2021). An important, but understudied component of this pipeline is the analysis of technical variation within this complex data source - intensity normalization is one way to remove this technical variability. The combination of disparate pre-processing pipelines, imaging variables, optical effects, and within-slide dependencies create batch and slide effects that can be reduced via normalization methods. Current state-of-the-art methods vary heavily across research labs and image acquisition platforms, without one singular method that is uniformly robust - optimal statistical methods seek to improve similarity across images and slides by removing this technical variability while maintaining the underlying biological signal in the data. mxnorm is open-source software built with R and S3 methods that implements, evaluates, and visualizes normalization techniques for multiplexed imaging data. Extending methodology described in Harris et al. (2022), we intend to set a foundation for the evaluation of multiplexed imaging normalization methods in R. This easily allows users to extend normalization methods into the field, and provides a robust evaluation framework to measure both technical variability and the efficacy of various normalization methods. One key component of the R package is the ability to supply user-defined normalization methods and thresholding algorithms to assess normalization in multiplexed imaging data. Core features, usage details, and extensive tutorials are available in the package documentation and vignette on CRAN and the software repository.