scholarly journals Multiplex, quantitative cellular analysis in large tissue volumes with clearing-enhanced 3D microscopy (Ce3D)

2017 ◽  
Vol 114 (35) ◽  
pp. E7321-E7330 ◽  
Author(s):  
Weizhe Li ◽  
Ronald N. Germain ◽  
Michael Y. Gerner
Keyword(s):  
Quantitative Analysis ◽  
High Resolution ◽  
Spatial Organization ◽  
Quantitative Imaging ◽  
Cell Types ◽  
Cell Populations ◽  
Lymphoid Tissues ◽  
Protein Fluorescence ◽  
Cellular Analysis ◽  
Multiple Cell

Organ homeostasis, cellular differentiation, signal relay, and in situ function all depend on the spatial organization of cells in complex tissues. For this reason, comprehensive, high-resolution mapping of cell positioning, phenotypic identity, and functional state in the context of macroscale tissue structure is critical to a deeper understanding of diverse biological processes. Here we report an easy to use method, clearing-enhanced 3D (Ce3D), which generates excellent tissue transparency for most organs, preserves cellular morphology and protein fluorescence, and is robustly compatible with antibody-based immunolabeling. This enhanced signal quality and capacity for extensive probe multiplexing permits quantitative analysis of distinct, highly intermixed cell populations in intact Ce3D-treated tissues via 3D histo-cytometry. We use this technology to demonstrate large-volume, high-resolution microscopy of diverse cell types in lymphoid and nonlymphoid organs, as well as to perform quantitative analysis of the composition and tissue distribution of multiple cell populations in lymphoid tissues. Combined with histo-cytometry, Ce3D provides a comprehensive strategy for volumetric quantitative imaging and analysis that bridges the gap between conventional section imaging and disassociation-based techniques.

Quantitative Imaging of Femoral Bone Marrow Microenvironments Reveals a Heterogenous Distribution of Hematopoietic Stem and Progenitor Cells.

Blood ◽  
2009 ◽  
Vol 114 (22) ◽  
pp. 1455-1455
Author(s):  
Cesar Nombela-Arrieta ◽  
Brendan Harley ◽  
Gregory Pivarnik ◽  
John E Mahoney ◽  
Elena Levantini ◽  
...  
Keyword(s):  
Stem Cells ◽  
B Cells ◽  
Cell Biology ◽  
Conflicts Of Interest ◽  
Statistical Significance ◽  
Quantitative Imaging ◽  
Cell Types ◽  
Immunofluorescent Staining ◽  
Cell Populations ◽  
Hematopoietic Stem

Abstract Abstract 1455 Poster Board I-478 Sustained production of all mature blood cell types relies on the continuous proliferation and differentiation of a rare population of self-renewing, multipotent hematopoietic stem cells (HSCs). HSC maintenance and lineage differentiation are thought to be regulated by spatially confined niches, defined by cellular components, soluble regulators, and the extracellular matrix immediately surrounding stem cells. Identification of these microenvironments in which endogenous and transferred HSCs reside within the BM is a major challenge in stem cell biology with relevant clinical implications. Yet the extreme rarity of HSCs, their dynamic nature, and the lack of specific markers to identify them, have precluded an accurate definition of HSC niches to date. Quantitative imaging technologies such as Laser Scanning Cytometry (LSC) are designed for the automated analysis of large cell numbers at a single cell level with high resolution while preserving the morphological information lost in flow cytometry, therefore providing data of statistical significance even for rare cell populations such as HSCs. We have employed LSC to analyze the localization of both adoptively transferred and endogenous hematopoietic stem and progenitor cell (HSPC) populations inside whole longitudinal sections of murine femoral BM cavities. Our results indicate that, as previously suggested, purified HSPC (Lin−c-kit+Sca-1+) significantly accumulate in endosteal regions (ER) of BM cavities (within 100μm of inner bone surface) upon transplantation. Nevertheless, analysis of sufficient numbers of more differentiated cell subsets (Lin−c-kit+Sca-1− progenitors, pro B cells and mature B cells) indicated that these areas serve as homing sites for most hematopoietic cells, highlighting the limitations of any conclusions drawn on HSC niche identity from studies performed with transferred HSPC populations. Immunofluorescent staining of endogenous cell populations revealed a gradient in distribution of early hematopoietic progenitors (c-kit+), which accumulated in but were not restricted to ER regions. Of note, a vast majority (>80%) of HSPC (Bmi-GFPhic-kit+, or Lin−c-kit+Sca-1+),were found inside ER, although not directly adjacent to endosteal surfaces. Our studies define endosteal areas as tissue regions where HSPC reside in close proximity, but not necessarily in direct contact with a dense vascular network, osteoblastic cells and other potential niche cell types and growth factors currently under investigation. Disclosures No relevant conflicts of interest to declare.

scholarly journals MUNIn (Multiple cell-type UNifying long-range chromatin Interaction detector): a statistical framework for identifying long-range chromatin interactions from multiple cell types

2020 ◽  
Author(s):  
Weifang Liu ◽  
Armen Abnousi ◽  
Qian Zhang ◽  
Yun Li ◽  
Ming Hu ◽  
...  
Keyword(s):  
Long Range ◽  
Spatial Organization ◽  
Critical Role ◽  
Cell Types ◽  
Chromatin Interaction ◽  
Cell Type ◽  
Statistical Framework ◽  
Chromatin Interactions ◽  
The Status ◽  
Multiple Cell

AbstractChromatin spatial organization (interactome) plays a critical role in genome function. Deep understanding of chromatin interactome can shed insights into transcriptional regulation mechanisms and human disease pathology. One essential task in the analysis of chromatin interactomic data is to identify long-range chromatin interactions. Existing approaches, such as HiCCUPS, FitHiC/FitHiC2 and FastHiC, are all designed for analyzing individual cell types. None of them accounts for unbalanced sequencing depths and heterogeneity among multiple cell types in a unified statistical framework. To fill in the gap, we have developed a novel statistical framework MUNIn (Multiple cell-type UNifying long-range chromatin Interaction detector) for identifying long-range chromatin interactions from multiple cell types. MUNIn adopts a hierarchical hidden Markov random field (H-HMRF) model, in which the status (peak or background) of each interacting chromatin loci pair depends not only on the status of loci pairs in its neighborhood region, but also on the status of the same loci pair in other cell types. To benchmark the performance of MUNIn, we performed comprehensive simulation studies and real data analysis, and showed that MUNIn can achieve much lower false positive rates for detecting cell-type-specific interactions (33.1 - 36.2%), and much enhanced statistical power for detecting shared peaks (up to 74.3%), compared to uni-cell-type analysis. Our data demonstrated that MUNIn is a useful tool for the integrative analysis of interactomic data from multiple cell types.

scholarly journals Probe-Seq enables transcriptional profiling of specific cell types from heterogeneous tissue by RNA-based isolation

2019 ◽  
Author(s):  
Ryoji Amamoto ◽  
Mauricio D. Garcia ◽  
Emma R. West ◽  
Jiho Choi ◽  
Sylvain W. Lapan ◽  
...  
Keyword(s):  
Transcriptional Profiling ◽  
Cell Types ◽  
Cell Populations ◽  
Specific Cell ◽  
Rna Profiling ◽  
Complementary Method ◽  
Dissociated Cells ◽  
Multiple Cell ◽  
Heterogeneous Tissue

ABSTRACTRecent transcriptional profiling technologies are uncovering previously-undefined cell populations and molecular markers at an unprecedented pace. While single cell RNA (scRNA) sequencing is an attractive approach for unbiased transcriptional profiling of all cell types, a complementary method to isolate and sequence specific cell populations from heterogeneous tissue remains challenging. Here, we developed Probe-Seq, which allows deep transcriptional profiling of specific cell types isolated using RNA as the defining feature. Dissociated cells are labelled using fluorescent in situ hybridization (FISH) for RNA, and then isolated by fluorescent activated cell sorting (FACS). We used Probe-Seq to purify and profile specific cell types from mouse, human, and chick retinas, as well as the Drosophila midgut. Probe-Seq is compatible with frozen nuclei, making cell types within archival tissue immediately accessible. As it can be multiplexed, combinations of markers can be used to create specificity. Multiplexing also allows for the isolation of multiple cell types from one cell preparation. Probe-Seq should enable RNA profiling of specific cell types from any organism.

scholarly journals High-resolution TADs reveal DNA sequences underlying genome organization in flies

2017 ◽  
Author(s):  
Fidel Ramírez ◽  
Vivek Bhardwaj ◽  
José Villaveces ◽  
Laura Arrigoni ◽  
Björn A. Grüning ◽  
...  
Keyword(s):  
High Resolution ◽  
Dna Sequences ◽  
Spatial Organization ◽  
Molecular Mechanisms ◽  
Cell Types ◽  
Open Chromatin ◽  
Promoter Regions ◽  
Dna Motifs ◽  
3D Genome ◽  
Eukaryotic Chromatin

AbstractEukaryotic chromatin is partitioned into domains called TADs that are broadly conserved between species and virtually identical among cell types within the same species. Previous studies in mammals have shown that the DNA binding protein CTCF and cohesin contribute to a fraction of TAD boundaries. Apart from this, the molecular mechanisms governing this partitioning remain poorly understood. Using our new software, HiCExplorer, we annotated high-resolution (570 bp) TAD boundaries in flies and identified eight DNA motifs enriched at boundaries. Known insulator proteins bind five of these motifs while the remaining three motifs are novel. We find that boundaries are either at core promoters of active genes or at non-promoter regions of inactive chromatin and that these two groups are characterized by different sets of DNA motifs. Most boundaries are present at divergent promoters of constitutively expressed genes and the gene expression tends to be coordinated within TADs. In contrast to mammals, the CTCF motif is only present on 2% of boundaries in flies. We demonstrate that boundaries can be accurately predicted using only the motif sequences, along with open chromatin, suggesting that DNA sequence encodes the 3D genome architecture in flies. Finally, we present an interactive online database to access and explore the spatial organization of fly, mouse and human genomes, available at http://chorogeome.ie-freiburg.mpg.de.

Tissue Engineering: Controlling Spatial Organization of Multiple Cell Types in Defined 3D Geometries (Adv. Mater. 41/2012)

2012 ◽  
Vol 24 (41) ◽  
pp. 5542-5542
Author(s):  
Halil Tekin ◽  
Jefferson G. Sanchez ◽  
Christian Landeros ◽  
Karen Dubbin ◽  
Robert Langer ◽  
...  
Keyword(s):  
Tissue Engineering ◽  
Spatial Organization ◽  
Cell Types ◽  
Multiple Cell

scholarly journals Integrative transcriptomic analysis of SLE reveals IFN-driven cross-talk between immune cells

2020 ◽  
Author(s):  
Bharat Panwar ◽  
Benjamin J. Schmiedel ◽  
Shu Liang ◽  
Brandie White ◽  
Enrique Rodriguez ◽  
...  
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
Autoimmune Disease ◽  
Cross Talk ◽  
Immune Cell ◽  
Cell Types ◽  
Cell Populations ◽  
Multiple Cell ◽  
Classical Monocytes ◽  
Multiple Immune Cell

ABSTRACTThe systemic lupus erythematosus (SLE) is an incurable autoimmune disease disproportionately affecting women and may lead to damage in multiple different organs. The marked heterogeneity in its clinical manifestations is a major obstacle in finding targeted treatments and involvement of multiple immune cell types further increases this complexity. Thus, identifying molecular subtypes that best correlate with disease heterogeneity and severity as well as deducing molecular cross-talk among major immune cell types that lead to disease progression are critical steps in the development of more informed therapies for SLE. Here we profile and analyze gene expression of six major circulating immune cell types from patients with well-characterized SLE (classical monocytes (n=64), T cells (n=24), neutrophils (n=24), B cells (n=20), conventional (n=20) and plasmacytoid (n=22) dendritic cells) and from healthy control subjects. Our results show that the interferon (IFN) response signature was the major molecular feature that classified SLE patients into two distinct groups: IFN-signature negative (IFNneg) and positive (IFNpos). We show that the gene expression signature of IFN response was consistent (i) across all immune cell types, (ii) all single cells profiled from three IFNpos donors using single-cell RNA-seq, and (iii) longitudinal samples of the same patient. For a better understanding of molecular differences of IFNpos versus IFNneg patients, we combined differential gene expression analysis with differential Weighted Gene Co-expression Network Analysis (WGCNA), which revealed a relatively small list of genes from classical monocytes including two known immune modulators, one the target of an approved therapeutic for SLE (TNFSF13B/BAFF: belimumab) and one itself a therapeutic for Rheumatoid Arthritis (IL1RN: anakinra). For a more integrative understanding of the cross-talk among different cell types and to identify potentially novel gene or pathway connections, we also developed a novel gene co-expression analysis method for joint analysis of multiple cell types named integrated WGNCA (iWGCNA). This method revealed an interesting cross-talk between T and B cells highlighted by a significant enrichment in the expression of known markers of T follicular helper cells (Tfh), which also correlate with disease severity in the context of IFNpos patients. Interestingly, higher expression of BAFF from all myeloid cells also shows a strong correlation with enrichment in the expression of genes in T cells that may mark circulating Tfh cells or related memory cell populations. These cell types have been shown to promote B cell class-switching and antibody production, which are well-characterized in SLE patients. In summary, we generated a large-scale gene expression dataset from sorted immune cell populations and present a novel computational approach to analyze such data in an integrative fashion in the context of an autoimmune disease. Our results reveal the power of a hypothesis-free and data-driven approach to discover drug targets and reveal novel cross-talk among multiple immune cell types specific to a subset of SLE patients. This approach is immediately useful for studying autoimmune diseases and is applicable in other contexts where gene expression profiling is possible from multiple cell types within the same tissue compartment.

scholarly journals Controlling Spatial Organization of Multiple Cell Types in Defined 3D Geometries

2012 ◽  
Vol 24 (41) ◽  
pp. 5543-5547 ◽  
Author(s):  
Halil Tekin ◽  
Jefferson G. Sanchez ◽  
Christian Landeros ◽  
Karen Dubbin ◽  
Robert Langer ◽  
...  
Keyword(s):  
Spatial Organization ◽  
Cell Types ◽  
Multiple Cell

scholarly journals Three-dimensional reconstructions of haustoria in two parasitic plant species in the Orobanchaceae

2021 ◽  
Author(s):  
Natsumi Masumoto ◽  
Yuki Suzuki ◽  
Songkui Cui ◽  
Mayumi Wakazaki ◽  
Mayuko Sato ◽  
...  
Keyword(s):  
Spatial Organization ◽  
Three Dimensional ◽  
Parasitic Plants ◽  
Cell Types ◽  
Striga Hermonthica ◽  
Microscopy Observation ◽  
Internal Structures ◽  
Facultative Parasite ◽  
Multiple Cell ◽  
Structural Insights

Abstract Parasitic plants infect other plants by forming haustoria, specialized multicellular organs consisting of several cell types, each of which has unique morphological features and physiological roles associated with parasitism. Understanding the spatial organization of cell types is, therefore, of great importance in elucidating the functions of haustoria. Here, we report a three-dimensional (3-D) reconstruction of haustoria from two Orobanchaceae species, the obligate parasite Striga hermonthica infecting rice (Oryza sativa) and the facultative parasite Phtheirospermum japonicum infecting Arabidopsis (Arabidopsis thaliana). In addition, field-emission scanning electron microscopy observation revealed the presence of various cell types in haustoria. Our images reveal the spatial arrangements of multiple cell types inside haustoria and their interaction with host roots. The 3-D internal structures of haustoria highlight differences between the two parasites, particularly at the xylem connection site with the host. Our study provides cellular and structural insights into haustoria of S. hermonthica and P. japonicum and lays the foundation for understanding haustorium function.

scholarly journals Revealing Hi-C subcompartments by imputing high-resolution inter-chromosomal chromatin interactions

2018 ◽  
Author(s):  
Kyle Xiong ◽  
Jian Ma
Keyword(s):  
High Resolution ◽  
Genome Organization ◽  
Cell Types ◽  
High Coverage ◽  
Chromatin Interactions ◽  
Gm12878 Cell ◽  
Additional Cell ◽  
Multiple Cell ◽  
Spatial Genome Organization ◽  
Different Cell Types

AbstractThe higher-order genome organization and its variation in different cellular conditions remains poorly understood. Recent high-resolution genome-wide mapping of chromatin interactions using Hi-C has revealed that chromosomes in the human genome are spatially segregated into distinct subcompartments. However, due to the requirement on sequencing coverage of the Hi-C data to define subcompartments, to date subcompartment annotation is only available in the GM12878 cell line, making it impractical to compare Hi-C subcompartment patterns across multiple cell types. Here we develop a new computational approach, named Sniper, based on an autoencoder and multilayer perceptron classifier to infer subcompartments using typical Hi-C datasets with moderate coverage. We demonstrated that Sniper can accurately reveal subcompartments based on Hi-C datasets with moderate coverage and can significantly outperform an existing method that uses numerous epigenomic datasets as input features in GM12878. We applied Sniper to eight additional cell lines to identify the variation of Hi-C subcompartments across different cell types. Sniper revealed that chromosomal regions with conserved and more dynamic subcompartment annotations across cell types have different patterns of functional genomic features. This work demonstrates that Sniper is effective in identifying subcompartments without the need of high-coverage Hi-C data and has the potential to provide new insights into the spatial genome organization variation across different cell types.

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