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 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 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 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 Homogentisic acid-derived pigment as a biocompatible label for optoacoustic imaging of macrophages

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Ina Weidenfeld ◽  
Christian Zakian ◽  
Peter Duewell ◽  
Andriy Chmyrov ◽  
Uwe Klemm ◽  
...  
Keyword(s):  
Single Cells ◽  
Cell Types ◽  
Homogentisic Acid ◽  
Tissue Imaging ◽  
Whole Body ◽  
Whole Body Imaging ◽  
Optoacoustic Imaging ◽  
Pigment Formation ◽  
Functionally Diverse

Abstract Macrophages are one of the most functionally-diverse cell types with roles in innate immunity, homeostasis and disease making them attractive targets for diagnostics and therapy. Photo- or optoacoustics could provide non-invasive, deep tissue imaging with high resolution and allow to visualize the spatiotemporal distribution of macrophages in vivo. However, present macrophage labels focus on synthetic nanomaterials, frequently limiting their ability to combine both host cell viability and functionality with strong signal generation. Here, we present a homogentisic acid-derived pigment (HDP) for biocompatible intracellular labeling of macrophages with strong optoacoustic contrast efficient enough to resolve single cells against a strong blood background. We study pigment formation during macrophage differentiation and activation, and utilize this labeling method to track migration of pro-inflammatory macrophages in vivo with whole-body imaging. We expand the sparse palette of macrophage labels for in vivo optoacoustic imaging and facilitate research on macrophage functionality and behavior.

scholarly journals Single-cell analysis of circadian dynamics in tissue explants

2015 ◽  
Vol 26 (22) ◽  
pp. 3940-3945 ◽  
Author(s):  
Laura Lande-Diner ◽  
Jacob Stewart-Ornstein ◽  
Charles J. Weitz ◽  
Galit Lahav
Keyword(s):  
Circadian Clock ◽  
Single Cell ◽  
Cell Tracking ◽  
Single Cell Analysis ◽  
Single Cells ◽  
Tissue Imaging ◽  
Wide Field ◽  
Isolated Cells ◽  
Peripheral Tissues

Tracking molecular dynamics in single cells in vivo is instrumental to understanding how cells act and interact in tissues. Current tissue imaging approaches focus on short-term observation and typically nonendogenous or implanted samples. Here we develop an experimental and computational setup that allows for single-cell tracking of a transcriptional reporter over a period of >1 wk in the context of an intact tissue. We focus on the peripheral circadian clock as a model system and measure the circadian signaling of hundreds of cells from two tissues. The circadian clock is an autonomous oscillator whose behavior is well described in isolated cells, but in situ analysis of circadian signaling in single cells of peripheral tissues is as-yet uncharacterized. Our approach allowed us to investigate the oscillatory properties of individual clocks, determine how these properties are maintained among different cells, and assess how they compare to the population rhythm. These experiments, using a wide-field microscope, a previously generated reporter mouse, and custom software to track cells over days, suggest how many signaling pathways might be quantitatively characterized in explant models.

scholarly journals Single Cell Viewer (SCV): An interactive visualization data portal for single cell RNA sequence data

2019 ◽  
Author(s):  
Shuoguo Wang ◽  
Constance Brett ◽  
Mohan Bolisetty ◽  
Ryan Golhar ◽  
Isaac Neuhaus ◽  
...  
Keyword(s):  
Single Cell ◽  
Sequence Data ◽  
Single Cells ◽  
Link Type ◽  
Technological Advances ◽  
R Shiny ◽  
Data Volume ◽  
Exploratory Data ◽  
Cell Data ◽  
Shiny Application

AbstractMotivationThanks to technological advances made in the last few years, we are now able to study transcriptomes from thousands of single cells. These have been applied widely to study various aspects of Biology. Nevertheless, comprehending and inferring meaningful biological insights from these large datasets is still a challenge. Although tools are being developed to deal with the data complexity and data volume, we do not have yet an effective visualizations and comparative analysis tools to realize the full value of these datasets.ResultsIn order to address this gap, we implemented a single cell data visualization portal called Single Cell Viewer (SCV). SCV is an R shiny application that offers users rich visualization and exploratory data analysis options for single cell datasets.AvailabilitySource code for the application is available online at GitHub (http://www.github.com/neuhausi/single-cell-viewer) and there is a hosted exploration application using the same example dataset as this publication at http://periscopeapps.org/[email protected]; [email protected]

scholarly journals CIM-seq

2021 ◽  
Author(s):  
Nathanael Andrews ◽  
Martin Enge
Keyword(s):  
Single Cell ◽  
Single Cells ◽  
Likelihood Estimation ◽  
Cell Types ◽  
Data Sets ◽  
Target Tissue ◽  
Data Set ◽  
Rnaseq Data ◽  
The Given ◽  
Cell Data

Abstract CIM-seq is a tool for deconvoluting RNA-seq data from cell multiplets (clusters of two or more cells) in order to identify physically interacting cell in a given tissue. The method requires two RNAseq data sets from the same tissue: one of single cells to be used as a reference, and one of cell multiplets to be deconvoluted. CIM-seq is compatible with both droplet based sequencing methods, such as Chromium Single Cell 3′ Kits from 10x genomics; and plate based methods, such as Smartseq2. The pipeline consists of three parts: 1) Dissociation of the target tissue, FACS sorting of single cells and multiplets, and conventional scRNA-seq 2) Feature selection and clustering of cell types in the single cell data set - generating a blueprint of transcriptional profiles in the given tissue 3) Computational deconvolution of multiplets through a maximum likelihood estimation (MLE) to determine the most likely cell type constituents of each multiplet.

scholarly journals The role of incoherent feedforward circuits in regulating precision of event timing

2020 ◽  
Author(s):  
Supravat Dey ◽  
Sherin Kannoly ◽  
Pavol Bokes ◽  
John J Dennehy ◽  
Abhyudai Singh
Keyword(s):  
Single Cells ◽  
Threshold Level ◽  
Cellular Growth ◽  
Critical Threshold ◽  
E Coli ◽  
Cellular Events ◽  
Event Timing ◽  
Simple Model System ◽  
Cell Data

AbstractTriggering of cellular events often relies on the level of a key gene product crossing a critical threshold. Achieving precision in event timing in spite of noisy gene expression facilitates high-fidelity functioning of diverse processes from biomolecular clocks, apoptosis and cellular differentiation. Here we investigate the role of an incoherent feedforward circuit in regulating the time taken by a bacterial virus (bacteriophage lambda) to lyse an infected Escherichia coli cell. Lysis timing is the result of expression and accumulation of a single lambda protein (holin) in the E. coli cell membrane up to a critical threshold level, which triggers the formation of membrane lesions. This easily visualized process provides a simple model system for characterizing event-timing stochasticity in single cells. Intriguingly, lambda’s lytic pathway synthesizes two functionally opposite proteins: holin and antiholin from the same mRNA in a 2:1 ratio. Antiholin sequesters holin and inhibits the formation of lethal membrane lesions, thus creating an incoherent feedforward circuit. We develop and analyze a stochastic model for this feedforward circuit that considers correlated bursty expression of holin/antiholin, and their concentrations are diluted from cellular growth. Interestingly, our analysis shows the noise in timing is minimized when both proteins are expressed at an optimal ratio, hence revealing an important regulatory role for antiholin. These results are in agreement with single cell data, where removal of antiholin results in enhanced stochasticity in lysis timing.

scholarly journals Accurate sub-population detection and mapping across single cell experiments with PopCorn

2018 ◽  
Author(s):  
Yijie Wang ◽  
Jan Hoinka ◽  
Teresa M Przytycka
Keyword(s):  
Comparative Analysis ◽  
Single Cell ◽  
Single Cells ◽  
Novel Method ◽  
Cell Data

The identification of sub-populations of cells present in a sample and the comparison of such sub-populations across samples are among the most frequently performed analyzes of single-cell data. Current tools for these kinds of data, however, fall short in their ability to adequately perform these tasks. We introduce a novel method, PopCorn (single cell sub-Populations Comparison), allowing for the identification of sub-populations of cells present within individual experiments while simultaneously performing sub-populations mapping across these experiments. PopCorn utilizes several novel algorithmic solutions enabling the execution of these tasks with unprecedented precision. As such, PopCorn provides a much-needed tool for comparative analysis of populations of single cells.

scholarly journals Single cell network analysis with a mixture of Nested Effects Models

2018 ◽  
Author(s):  
Martin Pirkl ◽  
Niko Beerenwinkel
Keyword(s):  
Single Cell ◽  
New Technologies ◽  
Single Cells ◽  
R Package ◽  
Supplementary Information ◽  
Data Sets ◽  
Cell Network ◽  
A Cell ◽  
Supplementary Material ◽  
Cell Data

AbstractMotivationNew technologies allow for the elaborate measurement of different traits of single cells. These data promise to elucidate intra-cellular networks in unprecedented detail and further help to improve treatment of diseases like cancer. However, cell populations can be very heterogeneous.ResultsWe developed a mixture of Nested Effects Models (M&NEM) for single-cell data to simultaneously identify different cellular sub-populations and their corresponding causal networks to explain the heterogeneity in a cell population. For inference, we assign each cell to a network with a certain probability and iteratively update the optimal networks and cell probabilities in an Expectation Maximization scheme. We validate our method in the controlled setting of a simulation study and apply it to three data sets of pooled CRISPR screens generated previously by two novel experimental techniques, namely Crop-Seq and Perturb-Seq.AvailabilityThe mixture Nested Effects Model (M&NEM) is available as the R-package mnem at https://github.com/cbgethz/mnem/[email protected], [email protected] informationSupplementary data are available.online.

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