Biology subjects

Chen, Y.-A.

Publications and source records attributed to Chen, Y.-A..

30 records · Page 2Linked to original sources

bioRxiv2022

A Web-based Software Resource for Interactive Analysis of Multiplex Tissue Imaging Datasets

Highly multiplexed tissue imaging (MTI) are powerful spatial proteomics technologies that enable in situ single-cell characterization of tissues. However, analysis and visualization of MTI datasets remains challenging, and we developed the Galaxy-ME software hub to address this challenge. Galaxy-ME is a web-based, interactive software hub that enables end-to-end analysis and visualization of MTI datasets and is accessible to everyone. To demonstrate its utility, Galaxy-ME was used to analyze datasets obtained from multiple MTI assays in both normal and cancerous tissues. Galaxy-ME is a publicly available web resource.

bioinformatics↗

bioRxiv2022

Snaq: A Dynamic Snakemake Pipeline for Microbiome data analysis with QIIME2

Optimizing a protocol for 16S microbiome data analysis with QIIME2 is a challenging task for biologists with no programming experience. It involves a multi-step process, and multiple parameters and options that need to be tested and determined. In this article, we describe Snaq, a snakemake pipeline that helps automate and optimize 16S data analysis using QIIME2. Snaq offers an informative file naming system and automatically performs the analysis of a data set by downloading and installing the required databases and classifiers, all through a single command-line instruction. It works natively on Linux and Mac and on Windows through the use of containers, and is potentially extendable by adding new rules. This pipeline will substantially reduce the efforts in sending commands and prevent the confusion caused by the accumulation of analysis results due to testing multiple parameters.

bioinformatics↗

bioRxiv2021

Massively Parallel Selection of NanoCluster Beacons

NanoCluster Beacons (NCBs) are multicolor silver nanocluster probes whose fluorescence can be activated or tuned by a proximal DNA strand called the activator. While a single-nucleotide difference in a pair of activators can lead to drastically different activation outcomes, termed the polar opposite twins (POTs), it is difficult to discover new POT-NCBs using the conventional low-throughput characterization approaches. Here we report a high-throughput selection method that takes advantage of repurposed next-generation-sequencing (NGS) chips to screen the activation fluorescence of [~]40,000 activator sequences. We find the nucleobases at positions 7-12 of the 18-nucleotide-long activator are critical to creating bright NCBs and positions 4-6 and 2-4 are hotspots to generate yellow and red POTs, respectively. Based on these findings, we propose a "zipper bag model" that explains how these hotspots lead to the creation of distinct silver cluster chromophores and contribute to the difference in chromophore chemical yields. Combining high-throughput screening with machine learning algorithms, we establish a pipeline to rationally design bright and multicolor NCBs.

bioengineering↗

bioRxiv2021

The spatial landscape of progression and immunoediting in primary melanoma at single cell resolution

Cutaneous melanoma is a highly immunogenic malignancy, surgically curable at early stages, but life- threatening when metastatic. Here we integrate high-plex imaging, 3D high-resolution microscopy, and spatially-resolved micro-region transcriptomics to study immune evasion and immunoediting in primary melanoma. We find that recurrent cellular neighborhoods involving tumor, immune, and stromal cells change significantly along a progression axis involving precursor states, melanoma in situ, and invasive tumor. Hallmarks of immunosuppression are already detectable in precursor regions. When tumors become locally invasive, a consolidated and spatially restricted suppressive environment forms along the tumor-stromal boundary. This environment is established by cytokine gradients that promote expression of MHC-II and IDO1, and by PD1-PDL1 mediated cell contacts involving macrophages, dendritic cells, and T cells. A few millimeters away, cytotoxic T cells synapse with melanoma cells in fields of tumor regression. Thus, invasion and immunoediting can co-exist within a few millimeters of each other in a single specimen. STATEMENT OF SIGNIFICANCEThe reorganization of the tumor ecosystem in primary melanoma is an excellent setting in which to study immunoediting and immune evasion. Guided by classical histopathology, spatial profiling of proteins and mRNA reveals recurrent morphological and molecular features of tumor evolution that involve localized paracrine cytokine signaling and direct cell-cell contact.

cancer biology↗

bioRxiv2021

Stitching and registering highly multiplexed whole slide images of tissues and tumors using ASHLAR software

MotivationStitching microscope images into a mosaic is an essential step in the analysis and visualization of large biological specimens, particularly human and animal tissues. Recent approaches to highly-multiplexed imaging generate high-plex data from sequential rounds of lower-plex imaging. These multiplexed imaging methods promise to yield precise molecular single-cell data and information on cellular neighborhoods and tissue architecture. However, attaining mosaic images with single-cell accuracy requires robust image stitching and image registration capabilities that are not met by existing methods. ResultsWe describe the development and testing of ASHLAR, a Python tool for coordinated stitching and registration of 103 or more individual multiplexed images to generate accurate whole-slide mosaics. ASHLAR reads image formats from most commercial microscopes and slide scanners, and we show that it performs better than existing open source and commercial software. ASHLAR outputs standard OME-TIFF images that are ready for analysis by other open-source tools and recently developed image analysis pipelines. Availability and implementationASHLAR is written in Python and available under an MIT license at https://github.com/labsyspharm/ashlar. An informational website with user guides and test data is available at https://labsyspharm.github.io/ashlar/.

bioinformatics↗

bioRxiv2021

UnMICST: Deep learning with real augmentation for robust segmentation of highly multiplexed images of human tissues

Newly developed technologies have made it feasible to routinely collect highly multiplexed (20-60 channel) images at subcellular resolution from human tissues for research and diagnostic purposes. Extracting single cell data from such images requires efficient and accurate image segmentation, a challenging problem that has recently benefited from the use of deep learning. In this paper, we demonstrate two approaches to improving tissue segmentation that are applicable to multiple deep learning frameworks. The first uses "real augmentations" that comprise defocused and saturated image data collected on the same instruments as the actual data; using real augmentation improves model accuracy to a significantly greater degree than computational augmentation (Gaussian blurring). The second involves imaging the nuclear envelope to better identify nuclear outlines. The two approaches cumulatively and substantially improve segmentation on a wide range of tissue types and provide a set of improved segmentation models. We speculate that the use of real augmentations may have applications in image processing outside of microscopy.

bioinformatics↗

bioRxiv2021

Multiplexed 3D atlas of state transitions and immune interactions in colorectal cancer

Advanced solid cancers are complex assemblies of tumor, immune, and stromal cells characterized by high intratumoral variation. We use highly multiplexed tissue imaging, 3D reconstruction, spatial statistics, and machine learning to identify cell types and states underlying morphological features of known diagnostic and prognostic significance in colorectal cancer. Quantitation of these features in high-plex marker space reveals recurrent transitions from one tumor morphology to the next, some of which are coincident with long-range gradients in the expression of oncogenes and epigenetic regulators. At the tumor invasive margin, where tumor, normal, and immune cells compete, T-cell suppression involves multiple cell types and 3D imaging shows that seemingly localized 2D features such as tertiary lymphoid structures are commonly interconnected and have graded molecular properties. Thus, while cancer genetics emphasizes the importance of discrete changes in tumor state, whole-specimen imaging reveals large-scale morphological and molecular gradients analogous to those in developing tissues.

cancer biology↗

bioRxiv2021

Micro-region transcriptomics of fixed human tissue using Pick-Seq

Spatial transcriptomics and multiplexed imaging are complementary methods for studying tissue biology and disease. Recently developed spatial transcriptomic methods use fresh-frozen specimens but most diagnostic specimens, clinical trials, and tissue archives rely on formaldehyde-fixed tissue. Here we describe the Pick-Seq method for deep spatial transcriptional profiling of fixed tissue. Pick-Seq is a form of micro-region sequencing in which small regions of tissue, containing 5-20 cells, are mechanically isolated on a microscope and then sequenced. We demonstrate the use of Pick-Seq with several different fixed and frozen human specimens. Application of Pick-Seq to a human melanoma with complex histology reveals significant differences in transcriptional programs associated with tumor invasion, proliferation, and immuno-editing. Parallel imaging confirms changes in immuno-phenotypes and cancer cell states. This work demonstrates the ability of Pick-Seq to generate deep spatial transcriptomic data from fixed and archival tissue with multiplexed imaging in parallel.

cancer biology↗

bioRxiv2021

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

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.

bioinformatics↗

bioRxiv2020

Multiplexed proteomics and imaging of resolving and lethal SARS-CoV-2 infection in the lung

Normal tissue physiology and repair depends on communication with the immune system. Understanding this communication at the molecular level in intact tissue requires new methods. The consequences of SARS-CoV-2 infection, which can result in acute respiratory distress, thrombosis and death, has been studied primarily in accessible liquid specimens such as blood, sputum and bronchoalveolar lavage, all of which are peripheral to the primary site of infection in the lung. Here, we describe the combined use of multiplexed deep proteomics with multiplexed imaging to profile infection and its sequelae directly in fixed lung tissue specimens obtained from necropsy of infected animals and autopsy of human decedents. We characterize multiple steps in disease response from cytokine accumulation and protein phosphorylation to activation of receptors, changes in signaling pathways, and crosslinking of fibrin to form clots. Our data reveal significant differences between naturally resolving SARS-CoV-2 infection in rhesus macaques and lethal COVID-19 in humans. The approach we describe is broadly applicable to other tissues and diseases. SummaryProteomics of infected tissue reveals differences in inflammatory and thrombotic responses between resolving and lethal COVID-19.

pathology↗

bioRxiv2020

Interpretative guides for interacting with tissue atlas and digital pathology data using the Minerva browser

The recent development of highly multiplexed tissue imaging promises to substantially accelerate research into basic biology and human disease. Concurrently, histopathology in a clinical setting is undergoing a rapid transition to digital methods. Online tissue atlases involving highly multiplexed images of research and clinical specimens will soon join genomics as a systematic source of information on the molecular basis of disease and therapeutic response. However, even with recent advances in machine learning, experience with anatomic pathology shows that there is no immediate substitute for expert visual review, annotation, and description of tissue images. In this perspective we review the ecosystem of software available for analysis of tissue images and identify a need for interactive guides or "digital docents" that allow experts to help make complex images intelligible. We illustrate this idea using Minerva software and discuss how interactive image guides are being integrated into multi-omic browsers for effective dissemination of atlas data.

scientific communication and education↗

bioRxiv2019

Highly multiplexed immunofluorescence images and single-cell data of immune markers in tonsil and lung cancer

In this data descriptor, we document a dataset of multiplexed immunofluorescence images and derived single-cell measurements of immune lineage and other markers in formaldehyde-fixed and paraffin-embedded (FFPE) human tonsil and lung cancer tissue. We used tissue cyclic immunofluorescence (t-CyCIF) to generate fluorescence images which we artifact corrected using the BaSiC tool, stitched and registered using the ASHLAR algorithm, and segmented using ilastik software and MATLAB. We extracted single-cell features from these images using HistoCAT software. The resulting dataset can be visualized using image browsers and analyzed using high-dimensional, single-cell methods. This dataset is a valuable resource for biological discovery of the immune system in normal and diseased states as well as for the development of multiplexed image analysis and viewing tools.\n\nMETADATA SUMMARY\n\nO_TBL View this table:\norg.highwire.dtl.DTLVardef@6edf3eorg.highwire.dtl.DTLVardef@1026e07org.highwire.dtl.DTLVardef@85a7baorg.highwire.dtl.DTLVardef@c6d7e5org.highwire.dtl.DTLVardef@87fccf_HPS_FORMAT_FIGEXP M_TBL C_TBL

cancer biology↗