Biology subjects

Patterson, N. H.

Publications and source records attributed to Patterson, N. H..

14 recordsLinked to original sources

bioRxiv2025

Unmixing of Imaging Mass Spectrometry Measurements Using Microscopy-Informed Constraints

Imaging mass spectrometry (IMS) provides spatially resolved molecular information of organic tissue but can be limited by pixel signals mixing contributions from adjacent biological structures, e.g. of single cells and multicellular functional tissue units (FTUs). This paper proposes computational methods to predict mass spectral profiles of biological structures on the basis of IMS data by "unmixing" pixel-level signals, leveraging microscopy-based boundary information of these structures. By modeling each biological structure as having a unique mass spectrum, we formulate a linear mixing model and solve the corresponding inverse problem that unmixes blended signals. In particular, we cover both overdetermined and underdetermined linear system scenarios and compare ordinary least squares, nonnegative least squares, and singular value thresholding to a custom algorithm, coined Tissue-informed Unmixing of Labeled regions by Inverse Problem (TULIP), specifically tailored to IMS data. Validation on a synthetic in-situ single cell dataset and demonstration on a largescale kidney FTU dataset illustrate the potential of these methods for enhanced in-situ tissue structure analysis, e.g. in cellular and tissue studies.

bioinformatics↗

bioRxiv2024

Integrating Ambient Mass Spectrometry Imaging and Spatial Transcriptomics on the Same Cancer Tissues to Identify Gene-Metabolite Correlations

The combination of spatial metabolomics and spatial transcriptomics can reveal powerful connections between gene expression and metabolism in heterogeneous tissues, but section-to-section variability can convolute data integration. We present a novel method combining Desorption Electrospray Ionization Mass Spectrometry Imaging spatial metabolomics and spatial transcriptomics on the same tissue sections. We demonstrate this workflow on human breast and lung cancer tissues identifying correlations between metabolites and cancer-related RNA transcripts.

cancer biology↗

bioRxiv2024

Spatially Resolved in vivo CRISPR Screen Sequencing via Perturb-DBiT

Perturb-seq enabled the profiling of transcriptional effects of genetic perturbations in single cells but lacks the ability to examine the impact on tissue environments. We present Perturb-DBiT for simultaneous co- sequencing of spatial transcriptome and guide RNAs (gRNAs) on the same tissue section for in vivo CRISPR screen with genome-scale gRNA libraries, offering a comprehensive understanding of how genetic modifications affect cellular behavior and tissue architecture. This platform supports a variety of delivery vectors, gRNA library sizes, and tissue preparations, along with two distinct gRNA capture methods, making it adaptable to a wide range of experimental setups. In applying Perturb-DBiT, we conducted un-biased knockouts of tens of genes or at genome-wide scale across three cancer models. We mapped all gRNAs in individual colonies and corresponding transcriptomes in a human cancer metastatic colonization model, revealing clonal dynamics and cooperation. We also examined the effect of genetic perturbation on the tumor immune microenvironment in an immune-competent syngeneic model, uncovering differential and synergistic perturbations in promoting immune infiltration or suppression in tumors. Perturb-DBiT allows for simultaneously evaluating the impact of each knockout on tumor initiation, development, metastasis, histopathology, and immune landscape. Ultimately, it not only broadens the scope of genetic inquiry, but also lays the groundwork for developing targeted therapeutic strategies.

genomics↗

bioRxiv2024

Revealing In Situ Molecular Profiles of Glomerular Cell Types and Substructures with Integrated Imaging Mass Spectrometry and Multiplexed Immunofluorescence Microscopy

Glomeruli filter blood through the coordination of podocytes, mesangial cells, fenestrated endothelial cells, and the glomerular basement membrane. Cellular changes, such as podocyte loss, are associated with pathologies like diabetic kidney disease (DKD). However, little is known regarding the in situ molecular profiles of specific cell types and how these profiles change with disease. Matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) is well-suited for untargeted tissue mapping of a wide range of molecular classes. Additional imaging modalities can be integrated with MALDI IMS to associate these biomolecular distributions to specific cell types. Herein, we demonstrate an integrated workflow combining MALDI IMS and multiplexed immunofluorescence (MxIF) microscopy. High spatial resolution MALDI IMS (5 {micro}m pixel size) was used to determine lipid distributions within human glomeruli, revealing intra-glomerular lipid heterogeneity. Mass spectrometric data were linked to specific glomerular cell types through new methods that enable MxIF microscopy to be performed on the same tissue section following MALDI IMS without sacrificing signal quality from either modality. A combination of machine-learning approaches was assembled, enabling cell-type segmentation and identification based on MxIF data followed by the mining of cell type or cluster-associated MALDI IMS signatures using classification models and interpretable machine learning. This allowed the automated discovery of spatially specific biomarker candidates for glomerular substructures and cell types. Overall, the work presented here establishes a toolbox for probing molecular signatures of glomerular cell types and substructures within tissue microenvironments and provides a framework that applies to other kidney tissue features and organ systems.

biochemistry↗

bioRxiv2023

Addressing persistent challenges in digital image analysis of cancerous tissues

The National Cancer Institute (NCI) supports many research programs and consortia, many of which use imaging as a major modality for characterizing cancerous tissue. A trans-consortia Image Analysis Working Group (IAWG) was established in 2019 with a mission to disseminate imaging-related work and foster collaborations. In 2022, the IAWG held a virtual hackathon focused on addressing challenges of analyzing high dimensional datasets from fixed cancerous tissues. Standard image processing techniques have automated feature extraction, but the next generation of imaging data requires more advanced methods to fully utilize the available information. In this perspective, we discuss current limitations of the automated analysis of multiplexed tissue images, the first steps toward deeper understanding of these limitations, what possible solutions have been developed, any new or refined approaches that were developed during the Image Analysis Hackathon 2022, and where further effort is required. The outstanding problems addressed in the hackathon fell into three main themes: 1) challenges to cell type classification and assessment, 2) translation and visual representation of spatial aspects of high dimensional data, and 3) scaling digital image analyses to large (multi-TB) datasets. We describe the rationale for each specific challenge and the progress made toward addressing it during the hackathon. We also suggest areas that would benefit from more focus and offer insight into broader challenges that the community will need to address as new technologies are developed and integrated into the broad range of image-based modalities and analytical resources already in use within the cancer research community.

bioinformatics↗

bioRxiv2023

Omics Scale Quantitative Mass Spectrometry Imaging of Lipids in Brain Tissue using a Multi-Class Internal Standard Mixture

Mass spectrometry imaging (MSI) has accelerated the understanding of lipid metabolism and spatial distribution in tissues and cells. However, few MSI studies have approached lipid imaging quantitatively and those that have focus on a single lipid class. Herein, we overcome limitation of quantitative MSI (Q-MSI) by using a multi-class internal standard lipid mixture that is sprayed homogenously over the tissue surface with analytical concentrations that reflects endogenous brain lipid levels. Using this approach we have performed Q-MSI for 13 lipid classes representing >200 sum-composition lipid species. This was carried out using both MALDI (negative ion mode) and MALDI-2 (positive ion mode) and pixel-wise normalisation of each lipid species signal to the corresponding class-specific IS an approach analogous to that widely used for shotgun lipidomics from biological extracts. This approach allows pixel concentrations of lipids to be reported in pmol/mm2. Q-MSI of lipids covered 3 orders of magnitude in dynamic range and revealed subtle change sin in distribution compared to conventional total-ion-current normalisation approaches. The robustness of the method was evaluated by repeating experiments in two laboratories on biological replicates using both timsTOF and Orbitrap mass spectrometers operated with a ~4-fold difference in mass resolution power. There was a strong overall correlation in the Q-MSI result obtained using the two approaches with outliers mostly rationalised by isobaric interferences that are only resolved with the Orbitrap system or the higher sensitivity of one instrument for particular lipid species, particularly for lipids detected at low intensity. These data provide insight into how mass resolving power can affect Q-MSI data. This approach opens up the possibility of performing large-scale Q-MSI studies across numerous lipid classes and reveal how absolute lipid concentrations vary throughout and between biological tissues.

biochemistry↗

bioRxiv2022

A multiscale atlas of the molecular and cellular architecture of the human kidney

Tissue atlases provide foundational knowledge on the cellular organization and molecular distributions across molecular classes and spatial scales. Here, we construct a comprehensive spatio-molecular lipid atlas of the human kidney from 29 donor tissues using integrated multimodal molecular imaging. Our approach leverages high spatial resolution matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry (IMS) for untargeted lipid mapping, stained microscopy for histopathological assessment, and tissue segmentation using autofluorescence microscopy. With a combination of unsupervised, supervised, and interpretive machine learning, the atlas provides multivariate lipid profiles of specific multicellular functional tissue units (FTUs) of the nephron, including the glomerulus, proximal tubules, thick ascending limb, distal tubules, and collecting ducts. In total, the atlas consists of tens of thousands of FTUs and millions of mass spectrometry measurements. Detailed patient, clinical, and histopathologic information allowed molecular data to be mined based on these features. As examples, we highlight the discovery of how lipid profiles are altered with sex and differences in body mass index.

biochemistry↗

bioRxiv2021

Tissue Registration and Exploration User Interfaces in support of a Human Reference Atlas

Several international consortia are collaborating to construct a human reference atlas, which is a comprehensive, high-resolution, three-dimensional atlas of all the cells in the healthy human body. Laboratories around the world are collecting tissue specimens from donors varying in sex, age, ethnicity, and body mass index. However, integrating and harmonizing tissue data across 20+ organs and more than 15 bulk and spatial single-cell assay types poses diverse challenges. Here we present the software tools and user interfaces developed to annotate ("register") and explore the collected tissue data. A key part of these tools is a common coordinate framework, which provides standard terminologies and data structures for describing specimens, biological structures, and spatial positions linked to existing ontologies. As of December 2021, the "registration" user interface has been used to harmonize and make publicly available data on 6,178 tissue sections from 2,698 tissue blocks collected by the Human Biomolecular Atlas Program, the Stimulating Peripheral Activity to Relieve Conditions program, the Human Cell Atlas, the Kidney Precision Medicine Project, and the Genotype Tissue Expression project. The second "exploration" user interface enables consortia to evaluate data quality and coverage, explore tissue data in the context of the human body, and guide data acquisition.

bioinformatics↗

bioRxiv2021

Robust and generalizable segmentation of human functional tissue units

The Human BioMolecular Atlas Program aims to compile a reference atlas for the healthy human adult body at the cellular level. Functional tissue units (FTU, e.g., renal glomeruli and colonic crypts) are of pathobiological significance and relevant for modeling and understanding disease progression. Yet, annotation of FTUs is time consuming and expensive when done manually and existing algorithms achieve low accuracy and do not generalize well. This paper compares the five winning algorithms from the "Hacking the Kidney" Kaggle competition to which more than a thousand teams from sixty countries contributed. We compare the accuracy and performance of the algorithms on a large-scale renal glomerulus Periodic acid-Schiff stain dataset and their generalizability to a colonic crypts hematoxylin and eosin stain dataset. Results help to characterize how the number of FTUs per unit area differs in relationship to their position in kidney and colon with respect to age, sex, body mass index (BMI), and other clinical data and are relevant for advancing pathology, anatomy, and surgery.

bioinformatics↗

bioRxiv2021

Autofluorescence microscopy as a label-free tool for renal histology and glomerular segmentation

Automated spatial segmentation models can enrich spatio-molecular omics analyses by providing a link to relevant biological structures. We developed segmentation models that use label-free autofluorescence (AF) microscopy to recognize multicellular functional tissue units (FTUs) (glomerulus, proximal tubule, descending thin limb, ascending thick limb, distal tubule, and collecting duct) and gross morphological structures (cortex, outer medulla, and inner medulla) in the human kidney. Annotations were curated using highly specific multiplex immunofluorescence and transferred to co-registered AF for model training. All FTUs (except the descending thin limb) and gross kidney morphology were segmented with high accuracy: >0.85 F1-score, and Dice-Sorensen coefficients >0.80, respectively. This workflow allowed lipids, profiled by imaging mass spectrometry, to be quantitatively associated with segmented FTUs. The segmentation masks were also used to acquire spatial transcriptomics data from collecting ducts. Consistent with previous literature, we demonstrated differing transcript expression of collecting ducts in the inner and outer medulla.

bioinformatics↗

bioRxiv2021

Tissue fixation effects on human retinal lipid analysis by MALDI imaging and LC-MS/MS technologies

Imaging mass spectrometry (IMS) allows the location and abundance of lipids to be mapped across tissue sections of human retina. For reproducible and accurate information, sample preparation methods need to be optimized. Paraformaldehyde fixation of a delicate multilayer structure like human retina facilitates the preservation of tissue morphology by forming methylene bridge cross-links between formaldehyde and amine/ thiols in biomolecules; however, retina sections analyzed by IMS are typically fresh-frozen. To determine if clinically significant inferences could be reliably based on fixed tissue, we evaluated the effect of fixation on analyte detection, spatial localization, and introduction of artefactual signals. Hence, we assessed the molecular identity of lipids generated by matrix-assisted laser desorption ionization (MALDI-IMS) and liquid chromatography coupled tandem mass spectrometry (LC-MS/MS) for fixed and fresh-frozen retina tissues in positive and negative ion modes. Based on MALDI-IMS analysis, more lipid signals were observed in fixed compared to fresh-frozen retina. More potassium adducts were observed in fresh-frozen tissues than fixed as the fixation process caused displacement of potassium adducts to protonated and sodiated species in ion positive ion mode. LC-MS/MS analysis revealed an overall decrease in lipid signals due to fixation that reduced glycerophospholipids and glycerolipids and conserved most sphingolipids and cholesteryl esters. The high quality and reproducible information from untargeted lipidomics analysis of fixed retina informs on all major lipid classes, similar to fresh-frozen retina, and serves as a steppingstone towards understanding of lipid alterations in retinal diseases.

biochemistry↗

bioRxiv2020

Automated Biomarker Candidate Discovery in Imaging Mass Spectrometry Data Through Spatially Localized Shapley Additive Explanations

The search for molecular species that are differentially expressed between biological states is an important step towards discovering promising biomarker candidates. In imaging mass spectrometry (IMS), performing this search manually is often impractical due to the large size and high-dimensionality of IMS datasets. Instead, we propose an interpretable machine learning workflow that automatically identifies biomarker candidates by their mass-to-charge ratios, and that quantitatively estimates their relevance to recognizing a given biological class using Shapley additive explanations (SHAP). The task of biomarker candidate discovery is translated into a feature ranking problem: given a classification model that assigns pixels to different biological classes on the basis of their mass spectra, the molecular species that the model uses as features are ranked in descending order of relative predictive importance such that the top-ranking features have a higher likelihood of being useful biomarkers. Besides providing the user with an experiment-wide measure of a molecular species biomarker potential, our workflow delivers spatially localized explanations of the classification models decision-making process in the form of a novel representation called SHAP maps. SHAP maps deliver insight into the spatial specificity of biomarker candidates by highlighting in which regions of the tissue sample each feature provides discriminative information and in which regions it does not. SHAP maps also enable one to determine whether the relationship between a biomarker candidate and a biological state of interest is correlative or anticorrelative. Our automated approach to estimating a molecular species potential for characterizing a user-provided biological class, combined with the untargeted and multiplexed nature of IMS, allows for the rapid screening of thousands of molecular species and the obtention of a broader biomarker candidate shortlist than would be possible through targeted manual assessment. Our biomarker candidate discovery workflow is demonstrated on mouse-pup and rat kidney case studies. HighlightsO_LIOur workflow automates the discovery of biomarker candidates in imaging mass spectrometry data by using state-of-the-art machine learning methodology to produce a shortlist of molecular species that are differentially expressed with regards to a user-provided biological class. C_LIO_LIA model interpretability method called Shapley additive explanations (SHAP), with observational Shapley values, enables us to quantify the local and global predictive importance of molecular species with respect to recognizing a user-provided biological class. C_LIO_LIBy providing spatially localized explanations for a classification models decision-making process, SHAP maps deliver insight into the spatial specificity of biomarker candidates and enable one to determine whether (and where) the relationship between a biomarker candidate and the class of interest is correlative or anticorrelative. C_LI

bioinformatics↗

bioRxiv2020

Highly Multiplexed Immunofluorescence of the Human Kidney using Co-Detection by Indexing (CODEX)

The human kidney is composed of many cell types that vary in their abundance and distribution from organ to organ. As these cell types perform unique and essential functions, it is important to confidently label each within a single tissue to more accurately assess tissue architecture. Towards this goal, we demonstrate the use of co-detection by indexing (CODEX) multiplexed immunofluorescence for visualizing 23 antigens within the human kidney. Using CODEX, many of the major cell types and substructures, such as collecting ducts, glomeruli, and thick ascending limb, were visualized within a single tissue section. Of these antibodies, 19 were conjugated in-house, demonstrating the flexibility and utility of this approach for studying the human kidney using traditional antibody markers. We performed a pilot study showing that the studied tissues had on average 84 {+/-} 11 cells per mm2 with the most variance seen within the cells containing vimentin and aquaporin 1, while cells containing -smooth muscle actin and CD31 possessed a high degree of uniformity between the samples. These precursory data show the power of CODEX multiplexed IF for surveying the cellular diversity of the human kidney and have potential applications within pathology, histology, and building anatomical atlases.

cell biology↗

bioRxiv2020

Lipid Landscape of the Human Retina and Supporting Tissues Revealed by High Resolution Imaging Mass Spectrometry

The human retina evolved to facilitate complex visual tasks. It supports vision at light levels ranging from starlight to sunlight, and its supporting tissues and vasculature regulate plasma-delivered lipophilic essentials for vision, including retinoids (vitamin A derivatives). The human retina is of particular interest because of its unique anatomic specializations for high-acuity and color vision that are also vulnerable to prevalent blinding diseases. The retinas exquisite cellular architecture is composed of numerous cell types that are aligned horizontally, giving rise to structurally distinct cell, synaptic, and vascular layers that are visible in histology and in diagnostic clinical imaging. Suitable for retinal investigations, MALDI imaging mass spectrometry (IMS) technologies are now capable of providing images at low micrometer spatial resolution with high levels of chemical specificity. In this study, a multimodal imaging approach combined with a recently developed method of high accuracy multi-image registration was used to define the localization of lipids in human retina tissue at laminar, cellular, and sub-cellular levels. Data acquired by IMS combined with autofluorescence and bright-field microscopy of human retina sections in macular and peripheral regions indicate differences in distributions and abundances of lipid species across and within single cell types. Of note is localization of signals within specific layers of macula, localization within different compartments of photoreceptors and RPE, complementarity of signals between macular retina and non-macular RPE, and evidence that lipids differing by a single double bond can have markedly different distributions.

biochemistry↗