Functional heterogeneity of lymphocytic patterns in primary melanoma dissected through single-cell multiplexing

In melanoma, the lymphocytic infiltrate is a prognostic parameter classified morphologically into ‘brisk’, ‘non-brisk’ and ‘absent’ entailing a functional association that has never been proved. Recently, it has been shown that lymphocytic populations can be very heterogeneous, and that anti-PD-1 immunotherapy supports activated T cells. Here, we characterize the immune landscape in primary melanoma by high-dimensional single-cell multiplex analysis in tissue sections (MILAN technique) followed by image analysis, RT-PCR and shotgun proteomics. We observed that the brisk and non-brisk patterns are heterogeneous functional categories that can be further sub-classified into active, transitional or exhausted. The classification of primary melanomas based on the functional paradigm also shows correlation with spontaneous regression, and an improved prognostic value when compared to that of the brisk classification. Finally, the main inflammatory cell subpopulations that are present in the microenvironment associated with activation and exhaustion and their spatial relationships are described using neighbourhood analysis.

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Functional heterogeneity of lymphocytic patterns in primary melanoma dissected through single-cell multiplexing

10.1101/409011 ◽

2018 ◽

Author(s):

Francesca Maria Bosisio ◽

Asier Antoranz ◽

Yannick van Herck ◽

Maddalena Maria Bolognesi ◽

Lukas Marcelis ◽

...

Keyword(s):

Single Cell ◽

Inflammatory Cell ◽

Primary Melanoma ◽

Shotgun Proteomics ◽

Functional Categories ◽

Activated T Cells ◽

Tissue Sections ◽

Neighbourhood Analysis ◽

Cell Subpopulations

AbstractIn melanoma, the lymphocytic infiltrate is a prognostic parameter classified morphologically into “brisk”, “non-brisk” and “absent” entailing a functional association that has never been proved. Recently, it has been shown that lymphocytic populations can be very heterogeneous, and that anti-PD-1 immunotherapy supports activated T cells. Here, we characterize the immune landscape in primary melanoma by high-dimensional single cell multiplex analysis in tissue sections (MILAN technique) followed by image analysis, RT-PCR and shotgun proteomics. We observed that the brisk and non-brisk patterns are heterogeneous functional categories that can be further sub-classified into active, transitional or exhausted. The classification of primary melanomas based on the functional paradigm also shows correlation with spontaneous regression, and an improved prognostic value than that of the brisk classification. Finally, the main inflammatory cell subpopulations that are present in the microenvironment associated with activation and exhaustion and their spatial relationships are described using neighbourhood analysis.

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Single-Cell RNAseq of Childhood Ependymoma Reveals Distinct Neoplastic Cell Subpopulations that Impact Etiology, Molecular Classification and Outcome

SSRN Electronic Journal ◽

10.2139/ssrn.3474066 ◽

2019 ◽

Author(s):

Austin Gillen ◽

Kent Riemondy ◽

Vladimir Amani ◽

Andrea Griesinger ◽

Ahmed Gilani ◽

...

Keyword(s):

Single Cell ◽

Molecular Classification ◽

Neoplastic Cell ◽

Cell Subpopulations

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Software Benchmark—Classification Tree Algorithms for Cell Atlases Annotation Using Single-Cell RNA-Sequencing Data

Microbiology Research ◽

10.3390/microbiolres12020022 ◽

2021 ◽

Vol 12 (2) ◽

pp. 317-334

Author(s):

Omar Alaqeeli ◽

Li Xing ◽

Xuekui Zhang

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Classification Tree ◽

Area Under The Curve ◽

Data Sets ◽

Sequencing Data ◽

Single Cell Rna Sequencing ◽

Tree Algorithms ◽

R Packages

Classification tree is a widely used machine learning method. It has multiple implementations as R packages; rpart, ctree, evtree, tree and C5.0. The details of these implementations are not the same, and hence their performances differ from one application to another. We are interested in their performance in the classification of cells using the single-cell RNA-Sequencing data. In this paper, we conducted a benchmark study using 22 Single-Cell RNA-sequencing data sets. Using cross-validation, we compare packages’ prediction performances based on their Precision, Recall, F1-score, Area Under the Curve (AUC). We also compared the Complexity and Run-time of these R packages. Our study shows that rpart and evtree have the best Precision; evtree is the best in Recall, F1-score and AUC; C5.0 prefers more complex trees; tree is consistently much faster than others, although its complexity is often higher than others.

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Sparsely-connected autoencoder (SCA) for single cell RNAseq data mining

npj Systems Biology and Applications ◽

10.1038/s41540-020-00162-6 ◽

2021 ◽

Vol 7 (1) ◽

Author(s):

Luca Alessandri ◽

Francesca Cordero ◽

Marco Beccuti ◽

Nicola Licheri ◽

Maddalena Arigoni ◽

...

Keyword(s):

Quality Control ◽

Single Cell ◽

Medical Personnel ◽

Cellular Heterogeneity ◽

Biological Information ◽

Cell Clusters ◽

The Neural Network ◽

Rnaseq Data ◽

Functional Features ◽

Cell Subpopulations

AbstractSingle-cell RNA sequencing (scRNAseq) is an essential tool to investigate cellular heterogeneity. Thus, it would be of great interest being able to disclose biological information belonging to cell subpopulations, which can be defined by clustering analysis of scRNAseq data. In this manuscript, we report a tool that we developed for the functional mining of single cell clusters based on Sparsely-Connected Autoencoder (SCA). This tool allows uncovering hidden features associated with scRNAseq data. We implemented two new metrics, QCC (Quality Control of Cluster) and QCM (Quality Control of Model), which allow quantifying the ability of SCA to reconstruct valuable cell clusters and to evaluate the quality of the neural network achievements, respectively. Our data indicate that SCA encoded space, derived by different experimentally validated data (TF targets, miRNA targets, Kinase targets, and cancer-related immune signatures), can be used to grasp single cell cluster-specific functional features. In our implementation, SCA efficacy comes from its ability to reconstruct only specific clusters, thus indicating only those clusters where the SCA encoding space is a key element for cells aggregation. SCA analysis is implemented as module in rCASC framework and it is supported by a GUI to simplify it usage for biologists and medical personnel.

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PhyliCS: a Python library to explore scCNA data and quantify spatial tumor heterogeneity

BMC Bioinformatics ◽

10.1186/s12859-021-04277-3 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Marilisa Montemurro ◽

Elena Grassi ◽

Carmelo Gabriele Pizzino ◽

Andrea Bertotti ◽

Elisa Ficarra ◽

...

Keyword(s):

Spatial Heterogeneity ◽

Single Cell ◽

Tumor Heterogeneity ◽

Third Party ◽

Sequencing Technology ◽

Reduction Methods ◽

Visualization Tools ◽

Cell Subpopulations ◽

Multiple Samples ◽

Analytical Approaches

Abstract Background Tumors are composed by a number of cancer cell subpopulations (subclones), characterized by a distinguishable set of mutations. This phenomenon, known as intra-tumor heterogeneity (ITH), may be studied using Copy Number Aberrations (CNAs). Nowadays ITH can be assessed at the highest possible resolution using single-cell DNA (scDNA) sequencing technology. Additionally, single-cell CNA (scCNA) profiles from multiple samples of the same tumor can in principle be exploited to study the spatial distribution of subclones within a tumor mass. However, since the technology required to generate large scDNA sequencing datasets is relatively recent, dedicated analytical approaches are still lacking. Results We present PhyliCS, the first tool which exploits scCNA data from multiple samples from the same tumor to estimate whether the different clones of a tumor are well mixed or spatially separated. Starting from the CNA data produced with third party instruments, it computes a score, the Spatial Heterogeneity score, aimed at distinguishing spatially intermixed cell populations from spatially segregated ones. Additionally, it provides functionalities to facilitate scDNA analysis, such as feature selection and dimensionality reduction methods, visualization tools and a flexible clustering module. Conclusions PhyliCS represents a valuable instrument to explore the extent of spatial heterogeneity in multi-regional tumour sampling, exploiting the potential of scCNA data.

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“High-content quantum dot-based subtype diagnosis and classification of breast cancer patients using hypermulticolor quantitative single cell imaging cytometry”

Nano Today ◽

10.1016/j.nantod.2012.06.009 ◽

2012 ◽

Vol 7 (4) ◽

pp. 231-244 ◽

Cited By ~ 20

Author(s):

Yu Kyung Tak ◽

Pravin K. Naoghare ◽

Byung Joo Kim ◽

Min Jung Kim ◽

Eun Sook Lee ◽

...

Keyword(s):

Breast Cancer ◽

Quantum Dot ◽

Single Cell ◽

Cancer Patients ◽

Cell Imaging ◽

Breast Cancer Patients ◽

Single Cell Imaging ◽

Imaging Cytometry ◽

Diagnosis And Classification

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EPEN-31. SINGLE-CELL RNAseq OF CHILDHOOD EPENDYMOMA REVEALS DISTINCT NEOPLASTIC CELL SUBPOPULATIONS THAT IMPACT ETIOLOGY, MOLECULAR CLASSIFICATION AND OUTCOME

Neuro-Oncology ◽

10.1093/neuonc/noaa222.167 ◽

2020 ◽

Vol 22 (Supplement_3) ◽

pp. iii314-iii314

Author(s):

Andrew Donson ◽

Austin Gillen ◽

Riemondy Kent ◽

Ahmed Gilani ◽

Sujatha Venkataraman ◽

...

Keyword(s):

Single Cell ◽

Histological Analysis ◽

Molecular Classification ◽

Neoplastic Cell ◽

Good Prognosis ◽

Cellular Heterogeneity ◽

Mesenchymal Phenotype ◽

Ependymal Differentiation ◽

Cell Subpopulations ◽

Cell Niche

Abstract Ependymoma (EPN) is a brain tumor commonly presenting in childhood that remains fatal in the majority of children. Intra-tumoral cellular heterogeneity in bulk-tumor samples significantly confounds our understanding of EPN biology, impeding development of effective therapy. We therefore used single-cell RNA sequencing to catalog cellular heterogeneity of 26 childhood EPN, predominantly from ST-RELA, PFA1 and PFA2 subgroups. ST-RELA and PFA subgroups clustered separately, with ST-RELA clustering largely according to individual sample-of-origin. PFA1 and PFA2 subgroup EPNs cells were intermixed and revealed 4 major subpopulations – 2 with characteristics of ependymal differentiation (transporter and ciliated phenotype subpopulations), an undifferentiated subpopulation and a mesenchymal phenotype. Pseudotime analysis showed the undifferentiated progenitor subpopulation either differentiating into ependymal differentiation subpopulations or transitioning into the mesenchymal subpopulation. Histological analysis revealed that undifferentiated and mesenchymal subpopulations cells colocalized to perinecrotic/perivascular zones, the putative ependymoma stem cell niche. Deconvolution of PFA bulk transcriptome data showed that undifferentiated and mesenchymal subpopulations were associated with a poor prognosis; whereas the ciliated ependymal cell-differentiated subpopulation was associated with a good prognosis. In conflict with current distinct classification paradigms, the ratio of mesenchymal and ciliated subpopulations determined bulk-tumor subgroups assignment to PFA1 and PFA2 respectively. This atlas of EPN cellular heterogeneity provides an important advance in our understanding of EPN biology, identifying high-risk associated subpopulations for therapeutic targeting.

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Category encoding method to select feature genes for the classification of bulk and single‐cell RNA‐seq data

Statistics in Medicine ◽

10.1002/sim.9015 ◽

2021 ◽

Author(s):

Yan Zhou ◽

Li Zhang ◽

Jinfeng Xu ◽

Jun Zhang ◽

Xiaodong Yan

Keyword(s):

Single Cell ◽

Rna Seq ◽

Encoding Method

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EPCO-06. AGE- AND REGION-SPECIFIC MULTI-OMIC CHARACTERIZATION OF H3-K27M MUTANT DIFFUSE MIDLINE GLIOMA

Neuro-Oncology ◽

10.1093/neuonc/noab196.005 ◽

2021 ◽

Vol 23 (Supplement_6) ◽

pp. vi2-vi2

Author(s):

Ilon Liu ◽

Jiang Li ◽

Daeun Jeong ◽

Olivia A Hack ◽

McKenzie Shaw ◽

...

Keyword(s):

Single Cell ◽

Immune Cell ◽

Age Groups ◽

Mesenchymal Cell ◽

Cell Compartments ◽

Histone 3 ◽

Age At Surgery ◽

Single Nucleus ◽

Pre Treatment ◽

Cell Subpopulations

Abstract Diffuse midline gliomas driven by lysine27-to-methionine mutations in histone 3 (H3-K27M DMGs) are among the most fatal brain tumors. Molecular studies including single cell RNA-sequencing (scRNA-seq) of pediatric and predominantly pontine H3-K27M DMGs have shown that the H3-K27M oncohistone keeps glioma cells locked in a stem-like oligodendrocyte precursor cell (OPC) state that is capable of self-renewal and tumor-initiation. However, a comprehensive dissection of the cellular architecture of H3-K27M DMGs across different midline regions and age groups is required to better understand the cell-intrinsic and contextual regulation of H3-K27M DMG cell identities. In particular, the more recently described group of adult H3-K27M DMGs remains understudied. Here, we have collected and characterized 45 H3-K27M mutant patient tumors, spanning pontine (n=26), thalamic (n=17), and spinal (n=2) locations. Median age at surgery was 12 (2-68) years, encompassing 21 early childhood (0-10 years), 12 adolescent (11-20 years), and 12 adult (≥ 21 years) tumors. The majority of samples were obtained pre-treatment (n=28), as opposed to post-treatment or at autopsy (n=17). We profiled all 45 tumors by single cell/single nucleus RNA-seq and selected tumors were further characterized by the single cell assay for transposase-accessible chromatin (scATAC-seq). Our integrated analyses highlight the predominance of transcriptionally and epigenetically defined OPC-like tumor cells as the main cell population of H3-K27M DMGs across all age groups and locations. We further identify distinct age- and location-specific OPC-like cell subpopulations. Comparison of pediatric and adult tumors further demonstrates a significant increase of mesenchymal cell states in adult H3-K27M DMGs, which we link to differences in glioma-associated immune cell compartments between age groups. Together, this study sheds light on the effects of age- and region-dependent microenvironments in shaping cellular identities in H3-K27M DMGs.

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