scholarly journals Using Imaging Mass Cytometry to Define Cell Identities and Interactions in Human Tissues

2021 ◽  
Vol 12 ◽  
Author(s):  
Vijayakumar R. Kakade ◽  
Marlene Weiss ◽  
Lloyd G. Cantley
Keyword(s):  
Imaging Techniques ◽  
Human Kidney ◽  
Cell Types ◽  
Cell Segmentation ◽  
Mass Cytometry ◽  
Multiplexed Imaging ◽  
Technical Details ◽  
Cellular Microenvironments ◽  
Antibody Validation ◽  
Diseased Kidney

In the evolving landscape of highly multiplexed imaging techniques that can be applied to study complex cellular microenvironments, this review characterizes the use of imaging mass cytometry (IMC) to study the human kidney. We provide technical details for antibody validation, cell segmentation, and data analysis specifically tailored to human kidney samples, and elaborate on phenotyping of kidney cell types and novel insights that IMC can provide regarding pathophysiological processes in the injured or diseased kidney. This review will provide the reader with the necessary background to understand both the power and the limitations of IMC and thus support better perception of how IMC analysis can improve our understanding of human disease pathogenesis and can be integrated with other technologies such as single cell sequencing and proteomics to provide spatial context to cellular data.

scholarly journals Dice-XMBD: Deep Learning-Based Cell Segmentation for Imaging Mass Cytometry

2021 ◽  
Vol 12 ◽  
Author(s):  
Xu Xiao ◽  
Ying Qiao ◽  
Yudi Jiao ◽  
Na Fu ◽  
Wenxian Yang ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Single Cell ◽  
Image Data ◽  
Basic Research ◽  
Cell Segmentation ◽  
Imaging Method ◽  
Imaging Data ◽  
Mass Cytometry ◽  
Multiplexed Imaging

Highly multiplexed imaging technology is a powerful tool to facilitate understanding the composition and interactions of cells in tumor microenvironments at subcellular resolution, which is crucial for both basic research and clinical applications. Imaging mass cytometry (IMC), a multiplex imaging method recently introduced, can measure up to 100 markers simultaneously in one tissue section by using a high-resolution laser with a mass cytometer. However, due to its high resolution and large number of channels, how to process and interpret the image data from IMC remains a key challenge to its further applications. Accurate and reliable single cell segmentation is the first and a critical step to process IMC image data. Unfortunately, existing segmentation pipelines either produce inaccurate cell segmentation results or require manual annotation, which is very time consuming. Here, we developed Dice-XMBD1, a Deep learnIng-based Cell sEgmentation algorithm for tissue multiplexed imaging data. In comparison with other state-of-the-art cell segmentation methods currently used for IMC images, Dice-XMBD generates more accurate single cell masks efficiently on IMC images produced with different nuclear, membrane, and cytoplasm markers. All codes and datasets are available at https://github.com/xmuyulab/Dice-XMBD.

scholarly journals Strategies for Accurate Cell Type Identification in CODEX Multiplexed Imaging Data

2021 ◽  
Vol 12 ◽  
Author(s):  
John W. Hickey ◽  
Yuqi Tan ◽  
Garry P. Nolan ◽  
Yury Goltsev
Keyword(s):  
Single Cell ◽  
Cell Biology ◽  
Clustering Algorithm ◽  
Cell Types ◽  
Unsupervised Clustering ◽  
Cell Segmentation ◽  
Imaging Data ◽  
Cell Type ◽  
Multiplexed Imaging ◽  
Four Levels

Multiplexed imaging is a recently developed and powerful single-cell biology research tool. However, it presents new sources of technical noise that are distinct from other types of single-cell data, necessitating new practices for single-cell multiplexed imaging processing and analysis, particularly regarding cell-type identification. Here we created single-cell multiplexed imaging datasets by performing CODEX on four sections of the human colon (ascending, transverse, descending, and sigmoid) using a panel of 47 oligonucleotide-barcoded antibodies. After cell segmentation, we implemented five different normalization techniques crossed with four unsupervised clustering algorithms, resulting in 20 unique cell-type annotations for the same dataset. We generated two standard annotations: hand-gated cell types and cell types produced by over-clustering with spatial verification. We then compared these annotations at four levels of cell-type granularity. First, increasing cell-type granularity led to decreased labeling accuracy; therefore, subtle phenotype annotations should be avoided at the clustering step. Second, accuracy in cell-type identification varied more with normalization choice than with clustering algorithm. Third, unsupervised clustering better accounted for segmentation noise during cell-type annotation than hand-gating. Fourth, Z-score normalization was generally effective in mitigating the effects of noise from single-cell multiplexed imaging. Variation in cell-type identification will lead to significant differential spatial results such as cellular neighborhood analysis; consequently, we also make recommendations for accurately assigning cell-type labels to CODEX multiplexed imaging.

scholarly journals Dice-XMBD: Deep learning-based cell segmentation for imaging mass cytometry

2021 ◽  
Author(s):  
Xu Xiao ◽  
Ying Qiao ◽  
Yudi Jiao ◽  
Na Fu ◽  
Wenxian Yang ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Single Cell ◽  
Image Data ◽  
Basic Research ◽  
Cell Segmentation ◽  
Imaging Method ◽  
Imaging Data ◽  
Mass Cytometry ◽  
Multiplexed Imaging

Highly multiplexed imaging technology is a powerful tool to facilitate understanding cells composition and interaction in tumor microenvironment at subcellular resolution, which is crucial for both basic research and clinical applications. Imaging mass cytometry (IMC), a multiplex imaging method recently introduced, can measure up to 40 markers simultaneously in one tissue section by using a high-resolution laser with a mass cytometer. However, due to its high resolution and large number of channels, how to process and interpret the image data from IMC remains a key challenge for its further applications. Accurate and reliable single cell segmentation is the first and a critical step to process IMC image data. Unfortunately, existing segmentation pipelines either produce inaccurate cell segmentation results, or require manual annotation which is very time-consuming. Here, we developed Dice-XMBD, a Deep learnIng-based Cell sEgmentation algorithm for tissue multiplexed imaging data. In comparison with other state-of-the-art cell segmentation methods currently used in IMC, Dice-XMBD generates more accurate single cell masks efficiently on IMC images produced with different nuclear, membrane and cytoplasm markers. All codes and datasets are available at https://github.com/xmuyulab/Dice-XMBD.

scholarly journals Single-cell RNA sequencing reveals the mesangial identity and species diversity of glomerular cell transcriptomes

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Bing He ◽  
Ping Chen ◽  
Sonia Zambrano ◽  
Dina Dabaghie ◽  
Yizhou Hu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Human Kidney ◽  
Cell Types ◽  
Model Organisms ◽  
Mural Cell ◽  
Glomerular Cell ◽  
Individual Gene ◽  
The Individual

AbstractMolecular characterization of the individual cell types in human kidney as well as model organisms are critical in defining organ function and understanding translational aspects of biomedical research. Previous studies have uncovered gene expression profiles of several kidney glomerular cell types, however, important cells, including mesangial (MCs) and glomerular parietal epithelial cells (PECs), are missing or incompletely described, and a systematic comparison between mouse and human kidney is lacking. To this end, we use Smart-seq2 to profile 4332 individual glomerulus-associated cells isolated from human living donor renal biopsies and mouse kidney. The analysis reveals genetic programs for all four glomerular cell types (podocytes, glomerular endothelial cells, MCs and PECs) as well as rare glomerulus-associated macula densa cells. Importantly, we detect heterogeneity in glomerulus-associated Pdgfrb-expressing cells, including bona fide intraglomerular MCs with the functionally active phagocytic molecular machinery, as well as a unique mural cell type located in the central stalk region of the glomerulus tuft. Furthermore, we observe remarkable species differences in the individual gene expression profiles of defined glomerular cell types that highlight translational challenges in the field and provide a guide to design translational studies.

scholarly journals Understanding the tumour immune microenvironment of stage III colorectal cancer using multiplexed imaging mass cytometry

Pathology ◽  
2021 ◽  
Vol 53 ◽  
pp. S36-S37
Author(s):  
Minh Tran ◽  
Andrew Su ◽  
HoJoon Lee ◽  
Richard Cruz ◽  
Lance Pflieger ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Stage Iii ◽  
Mass Cytometry ◽  
Immune Microenvironment ◽  
Multiplexed Imaging

scholarly journals Insulin-like growth factor binding protein 7 and tissue inhibitor of metalloproteinases-2: differential expression and secretion in human kidney tubule cells

2017 ◽  
Vol 312 (2) ◽  
pp. F284-F296 ◽  
Author(s):  
David R. Emlet ◽  
Nuria Pastor-Soler ◽  
Allison Marciszyn ◽  
Xiaoyan Wen ◽  
Hernando Gomez ◽  
...  
Keyword(s):  
Cell Culture ◽  
Acute Kidney Injury ◽  
Kidney Injury ◽  
Human Kidney ◽  
Distal Tubule ◽  
Cell Types ◽  
Tissue Inhibitor Of Metalloproteinases ◽  
Tubule Cell ◽  
Tubule Cells

We have characterized the expression and secretion of the acute kidney injury (AKI) biomarkers insulin-like growth factor binding protein 7 (IGFBP7) and tissue inhibitor of metalloproteinases-2 (TIMP-2) in human kidney epithelial cells in primary cell culture and tissue. We established cell culture model systems of primary kidney cells of proximal and distal tubule origin and observed that both proteins are indeed expressed and secreted in both tubule cell types in vitro. However, TIMP-2 is both expressed and secreted preferentially by cells of distal tubule origin, while IGFBP7 is equally expressed across tubule cell types yet preferentially secreted by cells of proximal tubule origin. In human kidney tissue, strong staining of IGFBP7 was seen in the luminal brush-border region of a subset of proximal tubule cells, and TIMP-2 stained intracellularly in distal tubules. Additionally, while some tubular colocalization of both biomarkers was identified with the injury markers kidney injury molecule-1 and neutrophil gelatinase-associated lipocalin, both biomarkers could also be seen alone, suggesting the possibility for differential mechanistic and/or temporal profiles of regulation of these early AKI biomarkers from known markers of injury. Last, an in vitro model of ischemia-reperfusion demonstrated enhancement of secretion of both markers early after reperfusion. This work provides a rationale for further investigation of these markers for their potential role in the pathogenesis of acute kidney injury.

scholarly journals Renal cell markers: lighthouses for managing renal diseases

2021 ◽  
Author(s):  
Shivangi Agarwal ◽  
Yashwanth R Sudhini ◽  
Onur K Polat ◽  
Jochen Reiser ◽  
Mehmet Mete Altintas
Keyword(s):  
High Efficiency ◽  
Imaging Techniques ◽  
Unmet Need ◽  
Cell Types ◽  
Whole Body ◽  
Renal Diseases ◽  
Cell Type ◽  
Cell Markers ◽  
Urine Production

Kidneys, one of the vital organs in our body, are responsible for maintaining whole-body homeostasis. The complexity of renal function (e.g., filtration, reabsorption, fluid and electrolyte regulation, urine production) demands diversity not only at the level of cell types but also in their overall distribution and structural framework within the kidney. To gain an in-depth molecular-level understanding of the renal system, it is imperative to discern the components of kidney and the types of cells residing in each of the sub-regions. Recent developments in labeling, tracing, and imaging techniques enabled us to mark, monitor and identify these cells in vivo with high efficiency in a minimally invasive manner. In this review, we have summarized different cell types, specific markers that are uniquely associated with those cell types, and their distribution in kidney, which altogether make kidneys so special and different. Cellular sorting based on the presence of certain proteins on the cell surface allowed for assignment of multiple markers for each cell type. However, different studies using different techniques have found contradictions in the cell-type specific markers. Thus, the term "cell marker" might be imprecise and sub-optimal, leading to uncertainty when interpreting the data. Therefore, we strongly believe that there is an unmet need to define the best cell markers for a cell type. Although, the compendium of renal-selective marker proteins presented in this review is a resource that may be useful to the researchers, we acknowledge that the list may not be necessarily exhaustive.

scholarly journals Simultaneous Multiplexed Imaging of mRNA and Proteins with Subcellular Resolution in Breast Cancer Tissue Samples by Mass Cytometry

Cell Systems ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 531 ◽  
Author(s):  
Daniel Schulz ◽  
Vito Riccardo Tomaso Zanotelli ◽  
Jana Raja Fischer ◽  
Denis Schapiro ◽  
Stefanie Engler ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Tissue ◽  
Cancer Tissue ◽  
Mass Cytometry ◽  
Tissue Samples ◽  
Multiplexed Imaging ◽  
Subcellular Resolution

scholarly journals A Toolbox to Characterize Human Induced Pluripotent Stem Cell–Derived Kidney Cell Types and Organoids

2019 ◽  
Vol 30 (10) ◽  
pp. 1811-1823 ◽  
Author(s):  
Jessica M. Vanslambrouck ◽  
Sean B. Wilson ◽  
Ker Sin Tan ◽  
Joanne Y.-C. Soo ◽  
Michelle Scurr ◽  
...  
Keyword(s):  
Kidney Cell ◽  
Human Kidney ◽  
Time Lapse ◽  
Cell Types ◽  
Isolation And Characterization ◽  
Gene And Protein Expression ◽  
Induced Pluripotent ◽  
Time Lapse Imaging ◽  
Kidney Morphogenesis ◽  
Kidney Organoids

BackgroundThe generation of reporter lines for cell identity, lineage, and physiologic state has provided a powerful tool in advancing the dissection of mouse kidney morphogenesis at a molecular level. Although use of this approach is not an option for studying human development in vivo, its application in human induced pluripotent stem cells (iPSCs) is now feasible.MethodsWe used CRISPR/Cas9 gene editing to generate ten fluorescence reporter iPSC lines designed to identify nephron progenitors, podocytes, proximal and distal nephron, and ureteric epithelium. Directed differentiation to kidney organoids was performed according to published protocols. Using immunofluorescence and live confocal microscopy, flow cytometry, and cell sorting techniques, we investigated organoid patterning and reporter expression characteristics.ResultsEach iPSC reporter line formed well patterned kidney organoids. All reporter lines showed congruence of endogenous gene and protein expression, enabling isolation and characterization of kidney cell types of interest. We also demonstrated successful application of reporter lines for time-lapse imaging and mouse transplantation experiments.ConclusionsWe generated, validated, and applied a suite of fluorescence iPSC reporter lines for the study of morphogenesis within human kidney organoids. This fluorescent iPSC reporter toolbox enables the visualization and isolation of key populations in forming kidney organoids, facilitating a range of applications, including cellular isolation, time-lapse imaging, protocol optimization, and lineage-tracing approaches. These tools offer promise for enhancing our understanding of this model system and its correspondence with human kidney morphogenesis.

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