scholarly journals MIAAIM: Multi-omics image integration and tissue state mapping using topological data analysis and cobordism learning

2021 ◽  
Author(s):  
Joshua M. Hess ◽  
Iulian Ilies ◽  
Denis Schapiro ◽  
John J. Iskra ◽  
Walid M. Abdelmoula ◽  
...  
Keyword(s):  
Single Cells ◽  
Tissue Imaging ◽  
Topological Data Analysis ◽  
Imaging Data ◽  
Mass Cytometry ◽  
Computational Framework ◽  
Multiple Imaging ◽  
Spatial Environment ◽  
Comprehensive Characterization ◽  
Detailed Molecular Analysis

High-parameter tissue imaging enables detailed molecular analysis of single cells in their spatial environment. However, the comprehensive characterization and mapping of tissue states through multimodal imaging across different physiological and pathological conditions requires data integration across multiple imaging systems. Here, we introduce MIAAIM (Multi-omics Image Alignment and Analysis by Information Manifolds) a modular, reproducible computational framework for aligning data across bioimaging technologies, modeling continuities in tissue states, and translating multimodal measures across tissue types. We demonstrate MIAAIM's workflows across diverse imaging platforms, including histological stains, imaging mass cytometry, and mass spectrometry imaging, to link cellular phenotypic states with molecular microenvironments in clinical biopsies from multiple tissue types with high cellular complexity. MIAAIM provides a robust foundation for the development of computational methods to integrate multimodal, high-parameter tissue imaging data and enable downstream computational and statistical interrogation of tissue states.

scholarly journals MCMICRO: a scalable, modular image-processing pipeline for multiplexed tissue imaging

2021 ◽  
Author(s):  
Denis Schapiro ◽  
Artem Sokolov ◽  
Clarence Yapp ◽  
Yu-An Chen ◽  
Jeremy L. Muhlich ◽  
...  
Keyword(s):  
Single Cells ◽  
Tissue Imaging ◽  
Processing Pipeline ◽  
Multiple Imaging ◽  
Multichannel Images ◽  
Solid Foundation ◽  
Reproducible Analysis ◽  
Cell Data ◽  
Whole Slide Images ◽  
Detailed Molecular Analysis

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

scholarly journals Spatially visualized single-cell pathology of highly multiplexed protein profiles in health and disease

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Mayar Allam ◽  
Thomas Hu ◽  
Shuangyi Cai ◽  
Krishnan Laxminarayanan ◽  
Robert B. Hughley ◽  
...  
Keyword(s):  
Single Cell ◽  
Biological Tissues ◽  
Chronic Tonsillitis ◽  
Tissue Imaging ◽  
Imaging Data ◽  
Mass Cytometry ◽  
Tissue Sections ◽  
Spatially Resolved ◽  
Cell Granularity ◽  
Health And Disease

AbstractDeep molecular profiling of biological tissues is an indicator of health and disease. We used imaging mass cytometry (IMC) to acquire spatially resolved 20-plex protein data in tissue sections from normal and chronic tonsillitis cases. We present SpatialViz, a suite of algorithms to explore spatial relationships in multiplexed tissue images by visualizing and quantifying single-cell granularity and anatomical complexity in diverse multiplexed tissue imaging data. Single-cell and spatial maps confirmed that CD68+ cells were correlated with the enhanced Granzyme B expression and CD3+ cells exhibited enrichment of CD4+ phenotype in chronic tonsillitis. SpatialViz revealed morphological distributions of cellular organizations in distinct anatomical areas, spatially resolved single-cell associations across anatomical categories, and distance maps between the markers. Spatial topographic maps showed the unique organization of different tissue layers. The spatial reference framework generated network-based comparisons of multiplex data from healthy and diseased tonsils. SpatialViz is broadly applicable to multiplexed tissue biology.

scholarly journals DRUG-NEM: Optimizing drug combinations using single-cell perturbation response to account for intratumoral heterogeneity

2018 ◽  
Vol 115 (18) ◽  
pp. E4294-E4303 ◽  
Author(s):  
Benedict Anchang ◽  
Kara L. Davis ◽  
Harris G. Fienberg ◽  
Brian D. Williamson ◽  
Sean C. Bendall ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cells ◽  
Drug Combinations ◽  
Intratumoral Heterogeneity ◽  
Combination Therapies ◽  
Mass Cytometry ◽  
Computational Framework ◽  
Single Drug ◽  
Perturbation Data ◽  
Minimum Number

An individual malignant tumor is composed of a heterogeneous collection of single cells with distinct molecular and phenotypic features, a phenomenon termed intratumoral heterogeneity. Intratumoral heterogeneity poses challenges for cancer treatment, motivating the need for combination therapies. Single-cell technologies are now available to guide effective drug combinations by accounting for intratumoral heterogeneity through the analysis of the signaling perturbations of an individual tumor sample screened by a drug panel. In particular, Mass Cytometry Time-of-Flight (CyTOF) is a high-throughput single-cell technology that enables the simultaneous measurements of multiple (>40) intracellular and surface markers at the level of single cells for hundreds of thousands of cells in a sample. We developed a computational framework, entitled Drug Nested Effects Models (DRUG-NEM), to analyze CyTOF single-drug perturbation data for the purpose of individualizing drug combinations. DRUG-NEM optimizes drug combinations by choosing the minimum number of drugs that produce the maximal desired intracellular effects based on nested effects modeling. We demonstrate the performance of DRUG-NEM using single-cell drug perturbation data from tumor cell lines and primary leukemia samples.

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

2021 ◽  
Author(s):  
Denis Schapiro ◽  
Artem Sokolov ◽  
Clarence Yapp ◽  
Jeremy L Muhlich ◽  
Joshua Hess ◽  
...  
Keyword(s):  
Single Cells ◽  
Tissue Imaging ◽  
Computational Pipeline ◽  
Processing Pipeline ◽  
Multiple Imaging ◽  
Solid Foundation ◽  
Reproducible Analysis ◽  
Cell Data ◽  
Different Tissues ◽  
Whole Slide Images

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.

scholarly journals Super-resolution chromatin tracing reveals domains and cooperative interactions in single cells

Science ◽  
2018 ◽  
Vol 362 (6413) ◽  
pp. eaau1783 ◽  
Author(s):  
Bogdan Bintu ◽  
Leslie J. Mateo ◽  
Jun-Han Su ◽  
Nicholas A. Sinnott-Armstrong ◽  
Mirae Parker ◽  
...  
Keyword(s):  
Spatial Organization ◽  
Single Cells ◽  
Domain Boundary ◽  
Super Resolution ◽  
Chromatin Domain ◽  
Imaging Method ◽  
Imaging Data ◽  
Chromatin Interactions ◽  
Population Average ◽  
Binding Factor

The spatial organization of chromatin is pivotal for regulating genome functions. We report an imaging method for tracing chromatin organization with kilobase- and nanometer-scale resolution, unveiling chromatin conformation across topologically associating domains (TADs) in thousands of individual cells. Our imaging data revealed TAD-like structures with globular conformation and sharp domain boundaries in single cells. The boundaries varied from cell to cell, occurring with nonzero probabilities at all genomic positions but preferentially at CCCTC-binding factor (CTCF)- and cohesin-binding sites. Notably, cohesin depletion, which abolished TADs at the population-average level, did not diminish TAD-like structures in single cells but eliminated preferential domain boundary positions. Moreover, we observed widespread, cooperative, multiway chromatin interactions, which remained after cohesin depletion. These results provide critical insight into the mechanisms underlying chromatin domain and hub formation.

scholarly journals Small-Sized Co-Polymers for Targeted Delivery of Multiple Imaging and Therapeutic Agents

Nanomaterials ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 2996
Author(s):  
Julia Y. Ljubimova ◽  
Arshia Ramesh ◽  
Liron L. Israel ◽  
Eggehard Holler
Keyword(s):  
Targeted Delivery ◽  
Therapeutic Agents ◽  
Tissue Imaging ◽  
Hydrodynamic Diameter ◽  
Blood Clearance ◽  
Deep Tissue ◽  
Flexible Polymer ◽  
Multiple Imaging ◽  
Binding Peptides ◽  
The Brain

Research has increasingly focused on the delivery of high, often excessive amounts of drugs, neglecting negative aspects of the carrier’s physical preconditions and biocompatibility. Among them, little attention has been paid to “small but beautiful” design of vehicle and multiple cargo to achieve effortless targeted delivery into deep tissue. The design of small biopolymers for deep tissue targeted delivery of multiple imaging agents and therapeutics (mini-nano carriers) emphasizes linear flexible polymer platforms with a hydrodynamic diameter of 4 nm to 10 nm, geometrically favoring dynamic juxtaposition of ligands to host receptors, and economic drug content. Platforms of biodegradable, non-toxic poly(β-l-malic acid) of this size carrying multiple chemically bound, optionally nature-derived or synthetic affinity peptides and drugs for a variety of purposes are described in this review with specific examples. The size, shape, and multiple attachments to membrane sites accelerate vascular escape and fast blood clearance, as well as the increase in medical treatment and contrasts for tissue imaging. High affinity antibodies routinely considered for targeting, such as the brain through the blood–brain barrier (BBB), are replaced by moderate affinity binding peptides (vectors), which penetrate at high influxes not achievable by antibodies.

scholarly journals Spatial Chemometrics and Comprehensive Chemical Imaging based Molecular Histopathology Delineates Anatomical Heterogeneity at Single Pixel Resolution

2020 ◽  
Author(s):  
Patrick Wehrli ◽  
Wojciech Michno ◽  
Laurent Guerard ◽  
Julia Fernandez-Rodriguez ◽  
Anders Bergh ◽  
...  
Keyword(s):  
Image Registration ◽  
Biological Tissues ◽  
Chemical Imaging ◽  
Chemical Information ◽  
Imaging Data ◽  
Pixel Resolution ◽  
Registration Method ◽  
Analysis Methods ◽  
Multiple Imaging ◽  
Single Pixel

<p>Imaging mass spectrometry (IMS) is a powerful tool for spatially-resolved chemical analysis and thereby offers novel perspectives for applications in biology and medicine. The understanding of chemically complex systems, such as biological tissues, benefits from the combination of multiple imaging modalities contributing with complementary molecular information. Effective analysis and interpretation of multimodal IMS data is challenging and requires both, precise alignment and combination of the imaging data as well as suitable statistical analysis methods to identify cross-modal correlations. Commonly applied IMS data analysis methods include qualitative comparative analysis where cross-modal interpretation is subject to human judgement; Workflows that incorporate image registration procedures are usually applied for co-representing data rather than to mine data across modalities. </p><p>Here, we present an IMS-based, histology-driven strategy for comprehensive interrogation of biological tissues by spatial chemometrics. Our workflow implements a 1+1-evolutionary image registration method enabling direct correlation of chemical information across multiple modalities at single pixel resolution. Comprehensive multimodal imaging data were evaluated using a novel approach based on orthogonal multiblock component analysis (OnPLS). Finally, we present a novel image fusion method by implementing consecutively acquired pathological staining data to enhance histological interpretation.</p><p>We demonstrate the method’s potential in two biomedical applications where trimodal matrix-assisted laser desorption/ionization (MALDI) IMS delineates pathology associated co-localization patterns of lipids and proteins in (1) a transgenic Alzheimer’s disease (AD) mouse model, and in (2) a human xenograft rat model of prostate cancer. The presented image analysis paradigm allows to comprehensively interrogate complex biological systems with single pixel resolution at cellular length scales.</p>

scholarly journals Topological Segmentation of Time-Varying Functional Connectivity Highlights the Role of Preferred Cortical Circuits

2020 ◽  
Author(s):  
Jacob Billings ◽  
Manish Saggar ◽  
Shella Keilholz ◽  
Giovanni Petri
Keyword(s):  
Functional Connectivity ◽  
Brain Function ◽  
Persistent Homology ◽  
Brain Regions ◽  
Topological Data Analysis ◽  
Time Varying ◽  
Imaging Data ◽  
Experimental Conditions ◽  
Common Time ◽  
Brain Imaging Data

Functional connectivity (FC) and its time-varying analogue (TVFC) leverage brain imaging data to interpret brain function as patterns of coordinating activity among brain regions. While many questions remain regarding the organizing principles through which brain function emerges from multi-regional interactions, advances in the mathematics of Topological Data Analysis (TDA) may provide new insights into the brain’s spontaneous self-organization. One tool from TDA, “persistent homology”, observes the occurrence and the persistence of n-dimensional holes presented in the metric space over a dataset. The occurrence of n-dimensional holes within the TVFC point cloud may denote conserved and preferred routes of information flow among brain regions. In the present study, we compare the use of persistence homology versus more traditional TVFC metrics at the task of segmenting brain states that differ across a common time-series of experimental conditions. We find that the structures identified by persistence homology more accurately segment the stimuli, more accurately segment volunteer performance during experimentally defined tasks, and generalize better across volunteers. Finally, we present empirical and theoretical observations that interpret brain function as a topological space defined by cyclic and interlinked motifs among distributed brain regions, especially, the attention networks.

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