PATH-21. THE SINGLE-CELL PATHOLOGY LANDSCAPE OF PEDIATRIC GLIOMA

Abstract High-grade glioma are the main cause of cancer-related death in children. Despite extensive research, their prognosis remains poor with very few treatment options. This can be attributed to the highly heterogeneous and plastic nature of glioma tumor cells and their interactions with the microenvironment, although quantitative data are still largely missing. Here, we used high-dimensional, multiplexed immunohistochemistry to map the spatial, single-cell tissue architecture of 31 pediatric glioma samples covering 9 histologic diagnoses. This novel approach allowed us to map the spatial distribution of the various tumoral subtypes, which typically occur in specific tumoral niches, and how these interact with their local immune-microenvironment. Finally, by aligning these findings to the clinical data of the patients and comparing these to adult glioblastoma, we are now able to more precisely describe the heterogeneous landscape of pediatric glioma at single-cell resolution.

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PATH-20. SPATIAL MAPPING OF THERAPY-INDUCED, PATHOLOGICAL CHANGES IN GLIOBLASTOMA AT SINGLE-CELL RESOLUTION

Neuro-Oncology ◽

10.1093/neuonc/noab196.472 ◽

2021 ◽

Vol 23 (Supplement_6) ◽

pp. vi119-vi119

Author(s):

Maxime Vanmechelen ◽

Jan Beckervordersandforth ◽

Jon Pey ◽

Asier Antoranz ◽

Pouya Nasari ◽

...

Keyword(s):

Single Cell ◽

Standard Of Care ◽

Tissue Architecture ◽

Heterogeneous Landscape ◽

Dismal Prognosis ◽

Cell Tissue ◽

Genomic Aberrations ◽

High Level ◽

Relationship Of ◽

The Relationship

Abstract Glioblastoma (GBM) remains a highly malignant, intrinsically resistant and inevitably recurring brain tumor with dismal prognosis. The aggressiveness and lack of effective GBM treatments can be attributed to the highly heterogeneous and plastic nature of GBM tumor cells, which easily confer resistance to standard-of-care (SOC) therapy. While tumor progression has also been attributed to interactions with the tumor microenvironment, quantitative data describing these interactions are still largely missing. Here, we used high-dimensional, multiplexed immunohistochemistry to map evolutions in the spatial, single-cell tissue architecture of 120 paired adult GBM tumor samples derived from 60 patients at diagnosis (ND) and upon recurrence (REC) following SOC treatment. We mapped the spatial distribution of a multitude of GBM tumoral subtypes across this multicentric cohort, through which we identified a high level of heterogeneity defined by specific tumoral niches within and across patients and which evolved when subjected to SOC therapy. In addition, we describe the relationship of the various tumoral niches with their local immune-infiltrates, highlighting an even more immunosuppressive environment following SOC resistance. Finally, by aligning these findings to the observed genomic aberrations and the clinical data of the patients, we are now able to more precisely describe the heterogeneous landscape of glioblastoma and how it evolves under SOC treatment at spatial, single-cell resolution.

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High‐dimensional single cell mapping of cerium distribution in the lung immune microenvironment of an active smoker

Cytometry Part B Clinical Cytometry ◽

10.1002/cyto.b.21545 ◽

2017 ◽

Vol 94 (6) ◽

pp. 941-945 ◽

Cited By ~ 6

Author(s):

Adeeb H. Rahman ◽

Yonit Lavin ◽

Soma Kobayashi ◽

Andrew Leader ◽

Miriam Merad

Keyword(s):

Single Cell ◽

High Dimensional ◽

Cell Mapping ◽

Immune Microenvironment ◽

Active Smoker

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SCHNEL: Scalable clustering of high dimensional single-cell data

10.1101/2020.03.30.015925 ◽

2020 ◽

Cited By ~ 1

Author(s):

Tamim Abdelaal ◽

Paul de Raadt ◽

Boudewijn P.F. Lelieveldt ◽

Marcel J.T. Reinders ◽

Ahmed Mahfouz

Keyword(s):

Single Cell ◽

Graph Clustering ◽

Original Data ◽

Large Datasets ◽

Supplementary Information ◽

High Dimensional ◽

Sequencing Data ◽

Novel Approach ◽

Cellular Markers ◽

Cell Data

AbstractMotivationSingle cell data measures multiple cellular markers at the single-cell level for thousands to millions of cells. Identification of distinct cell populations is a key step for further biological understanding, usually performed by clustering this data. Dimensionality reduction based clustering tools are either not scalable to large datasets containing millions of cells, or not fully automated requiring an initial manual estimation of the number of clusters. Graph clustering tools provide automated and reliable clustering for single cell data, but suffer heavily from scalability to large datasets.ResultsWe developed SCHNEL, a scalable, reliable and automated clustering tool for high-dimensional single-cell data. SCHNEL transforms large high-dimensional data to a hierarchy of datasets containing subsets of data points following the original data manifold. The novel approach of SCHNEL combines this hierarchical representation of the data with graph clustering, making graph clustering scalable to millions of cells. Using seven different cytometry datasets, SCHNEL outperformed three popular clustering tools for cytometry data, and was able to produce meaningful clustering results for datasets of 3.5 and 17.2 million cells within workable timeframes. In addition, we show that SCHNEL is a general clustering tool by applying it to single-cell RNA sequencing data, as well as a popular machine learning benchmark dataset MNIST.Availability and ImplementationImplementation is available on GitHub (https://github.com/paulderaadt/HSNE-clustering)[email protected] informationSupplementary data are available at Bioinformatics online.

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SCHNEL: scalable clustering of high dimensional single-cell data

Bioinformatics ◽

10.1093/bioinformatics/btaa816 ◽

2020 ◽

Vol 36 (Supplement_2) ◽

pp. i849-i856

Author(s):

Tamim Abdelaal ◽

Paul de Raadt ◽

Boudewijn P F Lelieveldt ◽

Marcel J T Reinders ◽

Ahmed Mahfouz

Keyword(s):

Single Cell ◽

Graph Clustering ◽

Original Data ◽

Large Datasets ◽

Supplementary Information ◽

High Dimensional ◽

Sequencing Data ◽

Novel Approach ◽

Cellular Markers ◽

Cell Data

Abstract Motivation Single cell data measures multiple cellular markers at the single-cell level for thousands to millions of cells. Identification of distinct cell populations is a key step for further biological understanding, usually performed by clustering this data. Dimensionality reduction based clustering tools are either not scalable to large datasets containing millions of cells, or not fully automated requiring an initial manual estimation of the number of clusters. Graph clustering tools provide automated and reliable clustering for single cell data, but suffer heavily from scalability to large datasets. Results We developed SCHNEL, a scalable, reliable and automated clustering tool for high-dimensional single-cell data. SCHNEL transforms large high-dimensional data to a hierarchy of datasets containing subsets of data points following the original data manifold. The novel approach of SCHNEL combines this hierarchical representation of the data with graph clustering, making graph clustering scalable to millions of cells. Using seven different cytometry datasets, SCHNEL outperformed three popular clustering tools for cytometry data, and was able to produce meaningful clustering results for datasets of 3.5 and 17.2 million cells within workable time frames. In addition, we show that SCHNEL is a general clustering tool by applying it to single-cell RNA sequencing data, as well as a popular machine learning benchmark dataset MNIST. Availability and implementation Implementation is available on GitHub (https://github.com/biovault/SCHNELpy). All datasets used in this study are publicly available. Supplementary information Supplementary data are available at Bioinformatics online.

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High-dimensional single cell-based immune profiling of the tumor immune microenvironment in prostate cancer.

Journal of Clinical Oncology ◽

10.1200/jco.2020.38.6_suppl.376 ◽

2020 ◽

Vol 38 (6_suppl) ◽

pp. 376-376

Author(s):

Michela Masetti ◽

Federica Portale ◽

Roberta Carriero ◽

Bianca Partini ◽

Nicolò Morina ◽

...

Keyword(s):

Gene Expression ◽

Prostate Cancer ◽

Flow Cytometry ◽

Single Cell ◽

High Dimensional ◽

Immune Microenvironment ◽

Single Cell Sequencing ◽

Single Cell Rna Sequencing ◽

Immune Changes ◽

Immune Profiling

376 Background: Genetic lesions that drive prostate cancer (PCa) development are able to modify the immune response and tumor infiltrating immune subsets, resulting in tumor progression. We investigated the profile of the immune microenvironment in PCa by high dimensional single cell analysis. Methods: We conducted an immune profiling study based on integrated RNA single cell sequencing and multiparametric flow cytometry in order to dissect the immune landscape of PCa. CD45+ immune cells infiltrating tumoral and adjacent non tumoral tissues were isolated from patients with PCa who underwent software assisted fusion biopsy, based on MRI, and/or radical prostatectomy, and analyzed by single cell sequencing. The primary endpoint was to evaluate the effectiveness of single cell RNA sequencing on CD45+ cell sorted from tumoral and adjacent non-tumoral tissues. Secondary endpoint was the identification of tumor-driven immune changes in prostatic lesions. Results: The cohort consisted of 3 patients who underwent radical prostatectomy (RP) and 45 patients with positive prostate biopsy; the negative control was checked by pathological assessment. In patients who underwent RP the gene expression analysis identified a modulation in the abundance of several immune subsets infiltrating the tumoral tissue, when compared with the non tumoral, evident for Tumor associated macrophages (TAMs), Natural Killer cells (NK) and T regulatory cells. We then implemented a 22 parameters flow cytometry panel that we tested on fresh prostatic tissue and peripheral blood from positive PCa biopsies. We identified a subset of tumor infiltrating macrophages showing an altered gene expression profile when compared with macrophages infiltrating the non-tumoral tissue. Importantly we derived a genetic signature from this subset of tumoral TAMs that resulted to be associated with cancer progression. Conclusions: Our findings support the effectiveness of single cell RNA sequencing in the dissection of the immune landscape in PCa and identified immune changes in patients when comparing neoplastic tissue with non tumoral areas. Such data may be useful for understanding the role of immune system in PCa carcinogenesis.

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Faculty Opinions recommendation of Integrating microarray-based spatial transcriptomics and single-cell RNA-seq reveals tissue architecture in pancreatic ductal adenocarcinomas.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.737201328.793570113 ◽

2020 ◽

Author(s):

Hans-Rudolf Hotz

Keyword(s):

Single Cell ◽

Rna Seq ◽

Tissue Architecture

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Elastic transformation of histological slices allows precise co-registration with microCT data sets for a refined virtual histology approach

Scientific Reports ◽

10.1038/s41598-021-89841-w ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Jonas Albers ◽

Angelika Svetlove ◽

Justus Alves ◽

Alexander Kraupner ◽

Francesca di Lillo ◽

...

Keyword(s):

Three Dimensional ◽

High Specificity ◽

Histopathological Analysis ◽

Specific Information ◽

Data Sets ◽

Virtual Histology ◽

Novel Approach ◽

Cell Tissue ◽

Immuno Histochemistry ◽

3D Information

AbstractAlthough X-ray based 3D virtual histology is an emerging tool for the analysis of biological tissue, it falls short in terms of specificity when compared to conventional histology. Thus, the aim was to establish a novel approach that combines 3D information provided by microCT with high specificity that only (immuno-)histochemistry can offer. For this purpose, we developed a software frontend, which utilises an elastic transformation technique to accurately co-register various histological and immunohistochemical stainings with free propagation phase contrast synchrotron radiation microCT. We demonstrate that the precision of the overlay of both imaging modalities is significantly improved by performing our elastic registration workflow, as evidenced by calculation of the displacement index. To illustrate the need for an elastic co-registration approach we examined specimens from a mouse model of breast cancer with injected metal-based nanoparticles. Using the elastic transformation pipeline, we were able to co-localise the nanoparticles to specifically stained cells or tissue structures into their three-dimensional anatomical context. Additionally, we performed a semi-automated tissue structure and cell classification. This workflow provides new insights on histopathological analysis by combining CT specific three-dimensional information with cell/tissue specific information provided by classical histology.

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Food waste treatment option selection through spherical fuzzy AHP

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-219178 ◽

2021 ◽

pp. 1-11

Author(s):

Aysu Melis Buyuk ◽

Gul T. Temur

Keyword(s):

Treatment Option ◽

Food Waste ◽

Waste Treatment ◽

Fuzzy Ahp ◽

Treatment Options ◽

Analytic Hierarchy ◽

Food Treatment ◽

Novel Approach ◽

Proposed Model ◽

Option Selection

In line with the increase in consciousness on sustainability in today’s global world, great emphasis has been attached to food waste management. Food waste is a complex issue to manage due to uncertainties on quality, quantity, location, and time of wastes, and it involves different decisions at many stages from seed to post-consumption. These ambiguities re-quire that some decisions should be handled in a linguistic and ambiguous environment. That forces researchers to benefit from fuzzy sets mostly utilized to deal with subjectivity that causes uncertainty. In this study, as a novel approach, the spherical fuzzy analytic hierarchy process (SFAHP) was used to select the best food treatment option. In the model, four main criteria (infrastructural, governmental, economic, and environmental) and their thirteen sub-criteria are considered. A real case is conducted to show how the proposed model can be used to assess four food waste treatment options (composting, anaerobic digestion, landfilling, and incineration). Also, a sensitivity analysis is generated to check whether the evaluations on the main criteria can change the results or not. The proposed model aims to create a subsidiary tool for decision makers in relevant companies and institutions.

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773 Adoptive Cell Therapy Response in Melanoma is Mediated by Stem-like CD8 T cells

Journal for ImmunoTherapy of Cancer ◽

10.1136/jitc-2020-sitc2020.0773 ◽

2020 ◽

Vol 8 (Suppl 3) ◽

pp. A822-A822

Author(s):

Sri Krishna ◽

Frank Lowery ◽

Amy Copeland ◽

Stephanie Goff ◽

Grégoire Altan-Bonnet ◽

...

Keyword(s):

T Cells ◽

T Cell ◽

Cell Therapy ◽

Single Cell ◽

Cd8 T Cells ◽

Tumor Infiltrating Lymphocytes ◽

High Dimensional ◽

Tumor Control ◽

Intrinsic Factors ◽

Infiltrating Lymphocytes

BackgroundAdoptive T cell therapy (ACT) utilizing ex vivo-expanded autologous tumor infiltrating lymphocytes (TILs) can result in complete regression of human cancers.1 Successful immunotherapy is influenced by several tumor-intrinsic factors.2 3 Recently, T cell-intrinsic factors have been associated with immunotherapy response in murine and human studies.4 5 Analyses of tumor-reactive TILs have concluded that anti-tumor neoantigen-specific TILs are enriched in subsets defined by the expression of PD-1 or CD39.6 7 Thus, there is a lack of consensus regarding the tumor-reactive TIL subset that is directly responsible for successful immunotherapies such as ICB and ACT. In this study, we attempted to define the fitness landscape of TIL-enriched infusion products to specifically understand its phenotypic impact on human immunotherapy responses.MethodsWe compared the phenotypic differences that could distinguish bulk ACT infusion products (I.P.) administered to patients who had complete response to therapy (complete responders, CRs, N = 24) from those whose disease progressed following ACT (non-responders, NRs, N = 30) by high dimensional single cell protein and RNA analysis of the I.P. We further analyzed the phenotypic states of anti-tumor neoantigen specific TILs from patient I.P (N = 26) by flow cytometry and single cell transcriptomics.ResultsWe identified two CD8+ TIL populations associated with clinical outcomes: a memory-progenitor CD39-negative stem-like TIL (CD39-CD69-) in the I.P. associated with complete cancer regression (overall survival, P < 0.0001, HR = 0.217, 95% CI 0.101 to 0.463) and TIL persistence, and a terminally differentiated CD39-positive TIL (CD39+CD69+) population associated with poor TIL persistence post-treatment. Although the majority (>65%) of neoantigen-reactive TILs in both responders and non-responders to ACT were found in the differentiated CD39+ state, CR infusion products also contained a pool of CD39- stem-like neoantigen-specific TILs (median = 8.8%) that was lacking in NR infusion products (median = 23.6%, P = 1.86 x 10-5). Tumor-reactive stem-like T cells were capable of self-renewal, expansion, and persistence, and mediated superior anti-tumor response in vivo.ConclusionsOur results support the hypothesis that responders to ACT received infusion products containing a pool of stem-like neoantigen-specific TILs that are able to undergo prolific expansion, give rise to differentiated subsets, and mediate long-term tumor control and T cell persistence, in line with recent murine ICB studies mediated by TCF+ progenitor T cells.4 5 Our data also suggest that TIL subsets mediating ACT-response (stem-like CD39-) might be distinct from TIL subsets enriched for anti-tumor-reactivity (terminally differentiated CD39+) in human TIL.6 7AcknowledgementsWe thank Don White for curating the melanoma patient cohort, and J. Panopoulos (Flowjo) for helpful discussions on high-dimensional analysis, and NCI Surgery Branch members for helpful insights and suggestions. S. Krishna acknowledges funding support from NCI Director’s Innovation Award from the National Cancer Institute.Trial RegistrationNAEthics ApprovalThe study was approved by NCI’s IRB ethics board.ReferencesGoff SL, et al. Randomized, prospective evaluation comparing intensity of lymphodepletion before adoptive transfer of tumor-infiltrating lymphocytes for patients with metastatic melanoma. J Clin Oncol 2016;34:2389–2397.Snyder A, et al. Genetic basis for clinical response to CTLA-4 blockade in melanoma. N Engl J Med 2014;371:2189–2199.McGranahan N, et al. Clonal neoantigens elicit T cell immunoreactivity and sensitivity to immune checkpoint blockade. Science 2016;351:1463–1469.Sade-Feldman M, et al. Defining T cell states associated with response to checkpoint immunotherapy in melanoma. Cell 2019;176:404.Miller BC, et al. Subsets of exhausted CD8 T cells differentially mediate tumor control and respond to checkpoint blockade. Nat. Immunol 2019;20:326–336.Simoni Y, et al. Bystander CD8 T cells are abundant and phenotypically distinct in human tumour infiltrates. Nature 2018;557:575–579.Gros A, et al. PD-1 identifies the patient-specific CD8+ tumor-reactive repertoire infiltrating human tumors. J Clin Invest 2014;124:2246–2259.

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