defaultStudy.json

{
  "studyList": [
    {
      "study": "HTA7_926_8_CRC01",
      "label": "High-plex immunofluorescence imaging and traditional histology of the same tissue section for discovering image-based biomarkers CRC01 (Lin et al Nature Cancer 2023)",
      "publication": [
        {
          "title": "High-plex immunofluorescence imaging and traditional histology of the same tissue section for discovering image-based biomarkers",
          "abstract": "Precision medicine is critically dependent on better methods for diagnosing and staging disease and predicting drug response. Histopathology using Hematoxylin and Eosin (H&E) stained tissue - not genomics – remains the primary diagnostic method in cancer. Recently developed highly-multiplexed tissue imaging methods promise to enhance research studies and clinical practice with precise, spatially-resolved, single-cell data. Here we describe the “Orion” platform for collecting H&E and high-plex immunofluorescence images from the same cells in a whole-slide format suitable for diagnosis. Using a retrospective cohort of 74 colorectal cancer resections, we show that IF and H&E images provide human experts and machine learning algorithms with complementary information that can be used to generate interpretable, multiplexed image-based models predictive of progression-free survival. Combining models of immune infiltration and tumor-intrinsic features achieves a nearly 20-fold discrimination between rapid and slow (or no) progression, demonstrating the ability of multi-modal tissue imaging to generate high-performance biomarkers.",
          "url": "https://www.tissue-atlas.org/atlas-datasets/lin-chen-campton-2023"
        }
      ],
      "cohortList": [
        {
          "cohort": "HTA7_926_8 CRC01",
          "donorNumber": 1,
          "preferredDataset": [
            {
              "host": "https://previewsinglecell.xenahubs.net",
              "name": "CRC/CRC01/P37_S29-CRC01.tsv"
            }
          ]
        }
      ]
    },
    
    {
      "study": "HTA7_934_9_CRC04",
      "label": "High-plex immunofluorescence imaging and traditional histology of the same tissue section for discovering image-based biomarkers CRC04 (Lin et al Nature Cancer 2023)",
      "publication": [
        {
          "title": "High-plex immunofluorescence imaging and traditional histology of the same tissue section for discovering image-based biomarkers",
          "abstract": "Precision medicine is critically dependent on better methods for diagnosing and staging disease and predicting drug response. Histopathology using Hematoxylin and Eosin (H&E) stained tissue - not genomics – remains the primary diagnostic method in cancer. Recently developed highly-multiplexed tissue imaging methods promise to enhance research studies and clinical practice with precise, spatially-resolved, single-cell data. Here we describe the “Orion” platform for collecting H&E and high-plex immunofluorescence images from the same cells in a whole-slide format suitable for diagnosis. Using a retrospective cohort of 74 colorectal cancer resections, we show that IF and H&E images provide human experts and machine learning algorithms with complementary information that can be used to generate interpretable, multiplexed image-based models predictive of progression-free survival. Combining models of immune infiltration and tumor-intrinsic features achieves a nearly 20-fold discrimination between rapid and slow (or no) progression, demonstrating the ability of multi-modal tissue imaging to generate high-performance biomarkers.",
          "url": "https://www.tissue-atlas.org/atlas-datasets/lin-chen-campton-2023"
        }
      ],
      "cohortList": [
        {
          "cohort": "HTA7_934_9 CRC04",
          "donorNumber": 1,
          "preferredDataset": [
            {
              "host": "https://previewsinglecell.xenahubs.net",
              "name": "CRC/CRC04/P37_S32-CRC04.tsv"
            }
          ]
        }
      ]
    },
    
    {
      "study": "Atlas_melanoma_MEL08",
      "label": "HTAN Melanoma ATLAS (Ajit et al Cancer Discovery 2022)",
      "publication": [
        {
          "title": "The spatial landscape of progression and immunoediting in primary melanoma at single cell resolution",
          "abstract": "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.",
          "url": "https://www.tissue-atlas.org/atlas-datasets/nirmal-maliga-vallius-2021/, https://aacrjournals.org/cancerdiscovery/article/doi/10.1158/2159-8290.CD-21-1357/698892/The-Spatial-Landscape-of-Progression-and"
        }
      ],
      "cohortList": [
        {
          "cohort": "MEL08",
          "donorNumber": 1,
          "preferredDataset": [
            {
              "host": "https://previewsinglecell.xenahubs.net",
              "name": "AtlasMelanoma/MEL08-1-1.tsv"
            }
          ]
        }
      ]
    },

    {
      "study": "msk_sclc_chan_2021",
      "label": "HTAN MSK - Single cell profiling reveals novel tumor and myeloid subpopulations in small cell lung cancer (Chan et al Cancer Cell 2021)",
      "description": "155,098 cells from 21 fresh SCLC clinical samples obtained from 19 patients, as well as 24 LUAD and 4 tumor-adjacent normal lung samples as controls. The SCLC and LUAD cohorts include treated and untreated patients. Samples were obtained from primary tumors, regional lymph node metastases, and distant metastases (liver, adrenal gland, axilla, and pleural effusion). This data was generated as part of the NCI Human Tumor Atlas Network. (Grant Number: 1U2CCA233284-01).",
      "publication": [
        {
          "title": "Signatures of plasticity, metastasis, and immunosuppression in an atlas of human small cell lung cancer",
          "abstract": "Small cell lung cancer (SCLC) is an aggressive malignancy that includes subtypes defined by differential expression of ASCL1, NEUROD1, and POU2F3 (SCLC-A, -N, and -P, respectively). To define the heterogeneity of tumors and their associated microenvironments across subtypes, we sequenced 155,098 transcriptomes from 21 human biospecimens, including 54,523 SCLC transcriptomes. We observe greater tumor diversity in SCLC than lung adenocarcinoma, driven by canonical, intermediate, and admixed subtypes. We discover a PLCG2-high SCLC phenotype with stem-like, pro-metastatic features that recurs across subtypes and predicts worse overall survival. SCLC exhibits greater immune sequestration and less immune infiltration than lung adenocarcinoma, and SCLC-N shows less immune infiltrate and greater T cell dysfunction than SCLC-A. We identify a profibrotic, immunosuppressive monocyte/macrophage population in SCLC tumors that is particularly associated with the recurrent, PLCG2-high subpopulation.",
          "url": "https://cellxgene.cziscience.com/collections/62e8f058-9c37-48bc-9200-e767f318a8ec"
        }
      ],
      "cohortList": [
        {
          "cohort": "HTAN MSK - Single cell profiling reveals novel tumor and myeloid subpopulations in small cell lung cancer",
          "donorNumber": 41,
          "preferredDataset": [
            {
              "host": "https://previewsinglecell.xenahubs.net",
              "name": "HTAN_MSK_scRNAseq/Combined_samples/exprMatrix.tsv"
            }
          ]
        }
      ]
    },
    
    {
      "study": "HTAN_breaset_CAF",
      "label": "HTAN Breast Cancer Metastases (Klughammer et al Nature Medicine 2024)",
      "publication": [
        {
          "title": "A multi-modal single-cell and spatial expression map of metastatic breast cancer biopsies across clinicopathological features",
          "abstract": "Although metastatic disease is the leading cause of cancer-related deaths, its tumor microenvironment remains poorly characterized due to technical and biospecimen limitations. In this study, we assembled a multi-modal spatial and cellular map of 67 tumor biopsies from 60 patients with metastatic breast cancer across diverse clinicopathological features and nine anatomic sites with detailed clinical annotations. We combined single-cell or single-nucleus RNA sequencing for all biopsies with a panel of four spatial expression assays (Slide-seq, MERFISH, ExSeq and CODEX) and H&E staining of consecutive serial sections from up to 15 of these biopsies. We leveraged the coupled measurements to provide reference points for the utility and integration of different experimental techniques and used them to assess variability in cell type composition and expression as well as emerging spatial expression characteristics across clinicopathological and methodological diversity. Finally, we assessed spatial expression and co-localization features of macrophage populations, characterized three distinct spatial phenotypes of epithelial-to-mesenchymal transition and identified expression programs associated with local T cell infiltration versus exclusion, showcasing the potential of clinically relevant discovery in such maps.",
          "url":"https://www.nature.com/articles/s41591-024-03215-z"
        }
      ],
      "cohortList": [
        {
          "cohort": "HTA1_878_7149_slideseq",
          "donorNumber": 1,
          "preferredDataset": [
            {
              "host": "https://previewsinglecell.xenahubs.net",
              "name": "HTAN_CAF/878_7149/slideseq/expression.tsv"
            }
          ]
        },
        {
          "cohort": "HTA1_878_7149_merfish",
          "donorNumber": 1,
          "preferredDataset": [
            {
              "host": "https://previewsinglecell.xenahubs.net",
              "name": "HTAN_CAF/878_7149/merfish/merfish_expression.tsv"
            }
          ]
        },
        {
          "cohort": "HTA1_878_7149_codex",
          "donorNumber": 1,
          "preferredDataset": [
            {
              "host": "https://previewsinglecell.xenahubs.net",
              "name": "HTAN_CAF/878_7149/codex/codex_expression.tsv"
            }
          ]
        },
        {
          "cohort": "HTA1_313_932_slideseq",
          "donorNumber": 1,
          "preferredDataset": [
            {
              "host": "https://previewsinglecell.xenahubs.net",
              "name": "HTAN_CAF/313_932/slideseq/expression.tsv"
            }
          ]
        },
        {
          "cohort": "HTA1_313_932_merfish",
          "donorNumber": 1,
          "preferredDataset": [
            {
              "host": "https://previewsinglecell.xenahubs.net",
              "name": "HTAN_CAF/313_932/merfish/merfish_expression.tsv"
            }
          ]
        },
        {
          "cohort": "HTA1_313_932_codex",
          "donorNumber": 1,
          "preferredDataset": [
            {
              "host": "https://previewsinglecell.xenahubs.net",
              "name": "HTAN_CAF/313_932/codex/codex_expression.tsv"
            }
          ]
        },
        {
          "cohort": "HTA1_997_7789_slideseq",
          "donorNumber": 1,
          "preferredDataset": [
            {
              "host": "https://previewsinglecell.xenahubs.net",
              "name": "HTAN_CAF/997_7789/slideseq/expression.tsv"
            }
          ]
        },
        {
          "cohort": "HTA1_997_7789_codex",
          "donorNumber": 1,
          "preferredDataset": [
            {
              "host": "https://previewsinglecell.xenahubs.net",
              "name": "HTAN_CAF/997_7789/codex/codex_expression.tsv"
            }
          ]
        },
        {
          "cohort": "HTA1_982_7629_slideseq",
          "donorNumber": 1,
          "preferredDataset": [
            {
              "host": "https://previewsinglecell.xenahubs.net",
              "name": "HTAN_CAF/982_7629/slideseq/expression.tsv"
            }
          ]
        },
        {
          "cohort": "HTA1_982_7629_merfish",
          "donorNumber": 1,
          "preferredDataset": [
            {
              "host": "https://previewsinglecell.xenahubs.net",
              "name": "HTAN_CAF/982_7629/merfish/merfish_expression.tsv"
            }
          ]
        },
        {
          "cohort": "HTA1_982_7629_codex",
          "donorNumber": 1,
          "preferredDataset": [
            {
              "host": "https://previewsinglecell.xenahubs.net",
              "name": "HTAN_CAF/982_7629/codex/codex_expression.tsv"
            }
          ]
        },
        {
          "cohort": "HTA1_944_7479_slideseq",
          "donorNumber": 1,
          "preferredDataset": [
            {
              "host": "https://previewsinglecell.xenahubs.net",
              "name": "HTAN_CAF/944_7479/slideseq/expression.tsv"
            }
          ]
        },
        {
          "cohort": "HTA1_944_7479_merfish",
          "donorNumber": 1,
          "preferredDataset": [
            {
              "host": "https://previewsinglecell.xenahubs.net",
              "name": "HTAN_CAF/944_7479/merfish/merfish_expression.tsv"
            }
          ]
        },
        {
          "cohort": "HTA1_917_4531_slideseq",
          "donorNumber": 1,
          "preferredDataset": [
            {
              "host": "https://previewsinglecell.xenahubs.net",
              "name": "HTAN_CAF/917_4531/slideseq/expression.tsv"
            }
          ]
        },
        {
          "cohort": "HTA1_917_4531_merfish",
          "donorNumber": 1,
          "preferredDataset": [
            {
              "host": "https://previewsinglecell.xenahubs.net",
              "name": "HTAN_CAF/917_4531/merfish/merfish_expression.tsv"
            }
          ]
        },
        {
          "cohort": "HTA1_917_4531_codex",
          "donorNumber": 1,
          "preferredDataset": [
            {
              "host": "https://previewsinglecell.xenahubs.net",
              "name": "HTAN_CAF/917_4531/codex/codex_expression.tsv"
            }
          ]
        },
        {
          "cohort": "HTA1_880_7179_slideseq",
          "donorNumber": 1,
          "preferredDataset": [
            {
              "host": "https://previewsinglecell.xenahubs.net",
              "name": "HTAN_CAF/880_7179/slideseq/expression.tsv"
            }
          ]
        },
        {
          "cohort": "HTA1_880_7179_merfish",
          "donorNumber": 1,
          "preferredDataset": [
            {
              "host": "https://previewsinglecell.xenahubs.net",
              "name": "HTAN_CAF/880_7179/merfish/merfish_expression.tsv"
            }
          ]
        },
        {
          "cohort": "HTA1_880_7179_codex",
          "donorNumber": 1,
          "preferredDataset": [
            {
              "host": "https://previewsinglecell.xenahubs.net",
              "name": "HTAN_CAF/880_7179/codex/codex_expression.tsv"
            }
          ]
        },
        {
          "cohort": "HTA1_853_4381_slideseq",
          "donorNumber": 1,
          "preferredDataset": [
            {
              "host": "https://previewsinglecell.xenahubs.net",
              "name": "HTAN_CAF/853_4381/slideseq/expression.tsv"
            }
          ]
        },
        {
          "cohort": "HTA1_853_4381_merfish",
          "donorNumber": 1,
          "preferredDataset": [
            {
              "host": "https://previewsinglecell.xenahubs.net",
              "name": "HTAN_CAF/853_4381/merfish/merfish_expression.tsv"
            }
          ]
        },
        {
          "cohort": "HTA1_853_4381_codex",
          "donorNumber": 1,
          "preferredDataset": [
            {
              "host": "https://previewsinglecell.xenahubs.net",
              "name": "HTAN_CAF/853_4381/codex/codex_expression.tsv"
            }
          ]
        },
        {
          "cohort": "HTA1_514_6760_slideseq",
          "donorNumber": 1,
          "preferredDataset": [
            {
              "host": "https://previewsinglecell.xenahubs.net",
              "name": "HTAN_CAF/514_6760/slideseq/expression.tsv"
            }
          ]
        },
        {
          "cohort": "HTA1_514_6760_merfish",
          "donorNumber": 1,
          "preferredDataset": [
            {
              "host": "https://previewsinglecell.xenahubs.net",
              "name": "HTAN_CAF/514_6760/merfish/merfish_expression.tsv"
            }
          ]
        },
        {
          "cohort": "HTA1_514_6760_codex",
          "donorNumber": 1,
          "preferredDataset": [
            {
              "host": "https://previewsinglecell.xenahubs.net",
              "name": "HTAN_CAF/514_6760/codex/codex_expression.tsv"
            }
          ]
        },
        {
          "cohort": "HTA1_812_8239_slideseq",
          "donorNumber": 1,
          "preferredDataset": [
            {
              "host": "https://previewsinglecell.xenahubs.net",
              "name": "HTAN_CAF/812_8239/slideseq/expression.tsv"
            }
          ]
        },
        {
          "cohort": "HTA1_812_8239_merfish",
          "donorNumber": 1,
          "preferredDataset": [
            {
              "host": "https://previewsinglecell.xenahubs.net",
              "name": "HTAN_CAF/812_8239/merfish/merfish_expression.tsv"
            }
          ]
        },
        {
          "cohort": "HTA1_812_8239_codex",
          "donorNumber": 1,
          "preferredDataset": [
            {
              "host": "https://previewsinglecell.xenahubs.net",
              "name": "HTAN_CAF/812_8239/codex/codex_expression.tsv"
            }
          ]
        }
      ]
    },
    
    {
      "study": "human_bone_marrow_Bandyopadhyay",
      "label": "Mapping the cellular biogeography of human bone marrow niches using single-cell transcriptomics and proteomic imaging (Bandyopadhyay et al Cell 2024)",
      "publication": [
        {
          "title": "Mapping the cellular biogeography of human bone marrow niches using single-cell transcriptomics and proteomic imaging",
          "abstract": "Non-hematopoietic cells are essential contributors to hematopoiesis. However, heterogeneity and spatial organization of these cells in human bone marrow remain largely uncharacterized. We used single-cell RNA sequencing (scRNA-seq) to profile 29,325 non-hematopoietic cells and discovered nine transcriptionally distinct subtypes. We simultaneously profiled 53,417 hematopoietic cells and predicted their interactions with non-hematopoietic subsets. We employed co-detection by indexing (CODEX) to spatially profile over 1.2 million cells. We integrated scRNA-seq and CODEX data to link predicted cellular signaling with spatial proximity. Our analysis revealed a hyperoxygenated arterio-endosteal neighborhood for early myelopoiesis, and an adipocytic localization for early hematopoietic stem and progenitor cells (HSPCs). We used our CODEX atlas to annotate new images and uncovered mesenchymal stromal cell (MSC) expansion and spatial neighborhoods co-enriched for leukemic blasts and MSCs in acute myeloid leukemia (AML) patient samples. This spatially resolved, multiomic atlas of human bone marrow provides a reference for investigation of cellular interactions that drive hematopoiesis.",
          "url": "https://www.cell.com/cell/fulltext/S0092-8674(24)00408-2"
        }
      ],
      "cohortList": [
        {
          "cohort": "human_bone_marrow_scRNAseq_Bandyopadhyay_H33",
          "donorNumber": 1,
          "preferredDataset": [
            {
              "host": "https://previewsinglecell.xenahubs.net",
              "name": "Bone_Marrow/scRNAseq/H33_seurat_rna_expression.tsv"
            }
          ]
        },
        {
          "cohort": "human_bone_marrow_CODEX_Bandyopadhyay_H33",
          "donorNumber": 1,
          "preferredDataset": [
            {
              "host": "https://previewsinglecell.xenahubs.net",
              "name": "Bone_Marrow/CODEX/H33_seurat_protein_expression.tsv"
            }
          ]
        }
      ]
    },
    
    {
      "study": "vz-ffpe-showcaseHuman-Lung",
      "label": "MERSCOPE FFPE Human Immuno-oncology Lung cancer",
      "publication": [
        {
          "title": "MERSCOPE FFPE Human Immuno-oncology",
          "url": "https://info.vizgen.com/ffpe-showcase"
        }
      ],
      "cohortList": [
        {
          "cohort": "Vizgen Showcase Human Lung Cancer Patient1",
          "donorNumber": 1,
          "preferredDataset": [
            {
              "host": "https://previewsinglecell.xenahubs.net",
              "name": "vz-ffpe-showcase/HumanLungCancerPatient1/expression.tsv"
            },
            {
              "host": "https://previewsinglecell.xenahubs.net",
              "name": "vz-ffpe-showcase/HumanLungCancerPatient1/expression_normed_scaled.tsv"
            }
          ]
        },
        {
          "cohort": "Vizgen Showcase Human Lung Cancer Patient2",
          "donorNumber": 1,
          "preferredDataset": [
            {
              "host": "https://previewsinglecell.xenahubs.net",
              "name": "vz-ffpe-showcase/HumanLungCancerPatient2/expression.tsv"
            },
            {
              "host": "https://previewsinglecell.xenahubs.net",
              "name": "vz-ffpe-showcase/HumanLungCancerPatient2/expression_normed_scaled.tsv"
            }
          ]
        }
      ]
    },
    
    {
      "study": "xenium_demo_hPancreas_Cancer",
      "label": "Pancreatic Cancer with Xenium Human Multi-Tissue and Cancer Panel (Xenium demo dataset)",
      "description": "Xenium In Situ Gene Expression data for human pancreatic cancer sections using the Xenium Human Multi-Tissue and Cancer Panel.",
      "publication": [
        {
          "title": "Pancreatic Cancer with Xenium Human Multi-Tissue and Cancer Panel",
          "url": "https://www.10xgenomics.com/datasets/pancreatic-cancer-with-xenium-human-multi-tissue-and-cancer-panel-1-standard"
        }
      ],
      "cohortList": [
        {
          "cohort": "xenium_hPancreas_Cancer",
          "donorNumber": 1,
          "preferredDataset": [
            {
              "host": "https://previewsinglecell.xenahubs.net",
              "name": "xenium/xenium_hPancreas_Cancer/exprMatrix.tsv"
            }
          ]
        }
      ]
    },

    {
      "study": "visiumHD_demo_human_colorectal_cancer",
      "label": "Human Colorectal Cancer, IF Stained (FFPE) (Visium HD demo dataset)",
      "publication": [
        {
          "title": "Visium HD Spatial Gene Expression Library, Human Colorectal Cancer (FFPE)",
          "url": "https://www.10xgenomics.com/datasets/visium-hd-cytassist-gene-expression-libraries-of-human-crc"
        }
      ],
      "cohortList": [
        {
          "cohort": "Visium HD Human Colorectal Cancer",
          "donorNumber": 1,
          "preferredDataset": [
            {
              "host": "https://previewsinglecell.xenahubs.net",
              "name": "VisiumHD/HumanColorectalCancer/exprMatrix.tsv"
            }
          ]
        }
      ]
    },
    
    {
      "study": "visiumHD_demo_human_lung_cancer",
      "label": "Human Lung Cancer, IF Stained (FFPE) (Visium HD demo dataset)",
      "publication": [
        {
          "title": "Visium HD Spatial Gene Expression Library, Human Lung Cancer, IF Stained (FFPE)",
          "url": "https://www.10xgenomics.com/datasets/visium-hd-cytassist-gene-expression-libraries-of-human-lung-cancer-if"
        }
      ],
      "cohortList": [
        {
          "cohort": "Visium HD Human Lung Cancer",
          "donorNumber": 1,
          "preferredDataset": [
            {
              "host": "https://previewsinglecell.xenahubs.net",
              "name": "VisiumHD/HumanLungCancer/exprMatrixCount.tsv"
            }
          ]
        }
      ]
    },
    
    {
      "study": "Vizgen MERFISH Mouse Brain Receptor Map",
      "label": "Mouse Brain Receptor Map (Vizgen demo dataset), integrated with Allen Brain scRNA-seq",
      "publication": [
        {
          "title": "MERFISH Mouse Brain Receptor Map",
          "abstract": "This dataset contains a MERFISH measurement of a gene panel containing 483 total genes including canonical brain cell type markers, GPCRs, and RTKs measured on 3 full coronal slices across 3 biological replicates.",
          "url": "https://info.vizgen.com/mouse-brain-map"
        }
      ],
      "cohortList": [
        {
          "cohort": "Vizgen MERFISH Mouse Brain Receptor Map S2R1",
          "donorNumber": 1,
          "preferredDataset": [
            {
              "host": "https://previewsinglecell.xenahubs.net",
              "name": "VizgenDemo/S2R1/exprMatrix.tsv"
            }
          ]
        },
        {
          "cohort": "Integration Vizgen MERFISH, Allen Brain scRNAseq Mouse Brain"
        }
      ]
    },
    
    {
      "study": "TB_McCaffrey",
      "label": "The immunoregulatory landscape of human tuberculosis granulomas (McCaffrey et al Nature Immunology 2022)",
      "publication": [
        {
          "title": "The immunoregulatory landscape of human tuberculosis granulomas",
          "url": "https://www.nature.com/articles/s41590-021-01121-x"
        }
      ],
      "cohortList": [
        {
          "cohort": "TB_McCaffrey_Patient10-1",
          "donorNumber": 1,
          "preferredDataset": [
            {
              "host": "https://previewsinglecell.xenahubs.net",
              "name": "TB_McCaffrey/Patient10-1/cells_p10-1.tsv"
            }
          ]
        }
      ]
    },
  
    {
      "study": "Wu_Swarbrick_breast_cancer",
      "label": "A single-cell and spatially resolved atlas of human breast cancers (Wu et al Nat Genet 2021)",
      "publication": [
        {
          "title": "A single-cell and spatially resolved atlas of human breast cancers",
          "url": "https://www.nature.com/articles/s41588-021-00911-1",
          "abstract":"Breast cancers are complex cellular ecosystems where heterotypic interactions play central roles in disease progression and response to therapy. However, our knowledge of their cellular composition and organization is limited. Here we present a single-cell and spatially resolved transcriptomics analysis of human breast cancers. We developed a single-cell method of intrinsic subtype classification (SCSubtype) to reveal recurrent neoplastic cell heterogeneity. Immunophenotyping using cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) provides high-resolution immune profiles, including new PD-L1/PD-L2+ macrophage populations associated with clinical outcome. Mesenchymal cells displayed diverse functions and cell-surface protein expression through differentiation within three major lineages. Stromal-immune niches were spatially organized in tumors, offering insights into antitumor immune regulation. Using single-cell signatures, we deconvoluted large breast cancer cohorts to stratify them into nine clusters, termed ‘ecotypes’, with unique cellular compositions and clinical outcomes. This study provides a comprehensive transcriptional atlas of the cellular architecture of breast cancer."
        }
      ],
      "cohortList": [
        {
          "cohort": "Wu_Swarbrick_breast_cancer_CID44971",
          "donorNumber": 1,
          "preferredDataset": [
            {
              "host": "https://previewsinglecell.xenahubs.net",
              "name": "Wu_Swarbrick_breast_cancer/CID44971/exprMatrix.tsv"
            }
          ]
        }
      ]
    },
    
    {
      "study": "cSCC",
      "label": "Human Squamous Cell Carcinoma (Ji et al Cell 2020, Zeira et al Nature Methods 2022, Chen et al BioRxiv 2022)",
      "publication": [
        {
          "title": "Multimodal Analysis of Composition and Spatial Architecture in Human Squamous Cell Carcinoma",
          "abstract": "To define the cellular composition and architecture of cutaneous squamous cell carcinoma (cSCC), we com- bined single-cell RNA sequencing with spatial transcriptomics and multiplexed ion beam imaging from a se- ries of human cSCCs and matched normal skin. cSCC exhibited four tumor subpopulations, three recapitu- lating normal epidermal states, and a tumor-specific keratinocyte (TSK) population unique to cancer, which localized to a fibrovascular niche. Integration of single-cell and spatial data mapped ligand-receptor net- works to specific cell types, revealing TSK cells as a hub for intercellular communication. Multiple features of potential immunosuppression were observed, including T regulatory cell (Treg) co-localization with CD8 T cells in compartmentalized tumor stroma. Finally, single-cell characterization of human tumor xenografts and in vivo CRISPR screens identified essential roles for specific tumor subpopulation-enriched gene net- works in tumorigenesis. These data define cSCC tumor and stromal cell subpopulations, the spatial niches where they interact, and the communicating gene networks that they engage in cancer.",
          "url": "https://pubmed.ncbi.nlm.nih.gov/32579974/"
        },
        {
          "title": "Alignment and integration of spatial transcriptomics data",
          "abstract": "Spatial transcriptomics (ST) measures mRNA expression across thousands of spots from a tissue slice while recording the two-dimensional (2D) coordinates of each spot. We introduce probabilistic alignment of ST experiments (PASTE), a method to align and integrate ST data from multiple adjacent tissue slices. PASTE computes pairwise alignments of slices using an optimal transport formulation that models both transcriptional similarity and physical distances between spots. PASTE further combines pairwise alignments to construct a stacked 3D alignment of a tissue. Alternatively, PASTE can integrate multiple ST slices into a single consensus slice. We show that PASTE accurately aligns spots across adjacent slices in both simulated and real ST data, demonstrating the advantages of using both transcriptional similarity and spatial information. We further show that the PASTE integrated slice improves the identification of cell types and differentially expressed genes compared with existing approaches that either analyze single ST slices or ignore spatial information.",
          "url": "https://pubmed.ncbi.nlm.nih.gov/35577957/"
        },
        {
          "title": "Visualizing somatic alterations in spatial transcriptomics data of skin cancer",
          "abstract": "Tools to visualize genetic alterations within tissues remain underdeveloped despite the growth of spatial transcriptomic technologies, which measure gene expression in different regions of tissues. Since genetic alterations can be detected in RNA-sequencing data, we explored the feasibility of observing somatic alterations in spatial transcriptomics data. Extracting genetic information from spatial transcriptomic data would illuminate the spatial distribution of clones and allow for correlations with regional changes in gene expression to support genotype- phenotype studies. Recent work demonstrates that copy number alterations can be inferred from spatial transcriptomics data1. Here, we describe new software to further enhance the inference of copy number from spatial transcriptomics data. Moreover, we demonstrate that single nucleotide variants are also detectable in spatial transcriptomic data. We applied these approaches to map the location of point mutations, copy number alterations, and allelic imbalances in spatial transcriptomic data of two cutaneous squamous cell carcinomas. We show that both tumors are dominated by a single clone of cells, suggesting that their regional variations in gene expression2 are likely driven by non-genetic factors. Furthermore, we observe mutant cells in histologically normal tissue surrounding one tumor, which were not discernible upon histopathologic evaluation. Finally, we detected mono-allelic expression of immunoglobulin heavy chains in B-cells, revealing clonal populations of plasma cells surrounding one tumor. In summary, we put forward solutions to add the genetic dimension to spatial transcriptomic datasets, augmenting the potential of this new technology.",
          "url": "https://doi.org/10.1101/2022.12.05.519162"
        }
      ],
      "cohortList": [
        {
          "cohort": "cSCC",
          "donorNumber": 2,
          "preferredDataset": [
            {
              "host": "https://previewsinglecell.xenahubs.net",
              "name": "cSCC/ST_Visium_exprMatrix.tsv"
            },
            {
              "host": "https://previewsinglecell.xenahubs.net",
              "name": "cSCC/ST_Visium_exprMatrix_count.tsv"
            }
          ]
        },
        {
          "cohort": "patient_2 center slice",
          "donorNumber": 1,
          "preferredDataset": [
            {
              "host": "https://previewsinglecell.xenahubs.net",
              "name": "cSCC/paste/patient_2/center_nmfs/exprMatrix.tsv"
            }
          ]
        },
        {
          "cohort": "patient_5 center slice",
          "donorNumber": 1,
          "preferredDataset": [
            {
              "host": "https://previewsinglecell.xenahubs.net",
              "name": "cSCC/paste/patient_5/center_nmfs/exprMatrix.tsv"
            }
          ]
        },
        {
          "cohort": "patient_9 center slice",
          "donorNumber": 1,
          "preferredDataset": [
            {
              "host": "https://previewsinglecell.xenahubs.net",
              "name": "cSCC/paste/patient_9/center_nmfs/exprMatrix.tsv"
            }
          ]
        },
        {
          "cohort": "patient_10 center slice",
          "donorNumber": 1,
          "preferredDataset": [
            {
              "host": "https://previewsinglecell.xenahubs.net",
              "name": "cSCC/paste/patient_10/center_nmfs/exprMatrix.tsv"
            }
          ]
        },
        {
          "cohort": "patient_2",
          "donorNumber": 1,
          "preferredDataset": [
            {
              "host": "https://previewsinglecell.xenahubs.net",
              "name": "cSCC/paste/patient_2/2D/exprMatrix.tsv"
            },
            {
              "host": "https://previewsinglecell.xenahubs.net",
              "name": "cSCC/paste/patient_2/2D/exprMatrix.log2.tsv"
            }
          ]
        },
        {
          "cohort": "patient_5",
          "donorNumber": 1,
          "preferredDataset": [
            {
              "host": "https://previewsinglecell.xenahubs.net",
              "name": "cSCC/paste/patient_5/2D/exprMatrix.tsv"
            }
          ]
        },
        {
          "cohort": "patient_9",
          "donorNumber": 1,
          "preferredDataset": [
            {
              "host": "https://previewsinglecell.xenahubs.net",
              "name": "cSCC/paste/patient_9/2D/exprMatrix.tsv"
            }
          ]
        },
        {
          "cohort": "patient_10",
          "donorNumber": 1,
          "preferredDataset": [
            {
              "host": "https://previewsinglecell.xenahubs.net",
              "name": "cSCC/paste/patient_10/2D/exprMatrix.tsv"
            }
          ]
        },
        {
          "cohort": "cSCC scRNA-seq",
          "donorNumber": 10,
          "preferredDataset": [
            {
              "host": "https://previewsinglecell.xenahubs.net",
              "name": "cSCC/scRNAseq_counts_log2.txt"
            },
            {
              "host": "https://previewsinglecell.xenahubs.net",
              "name": "cSCC/scRNAseq_counts.txt"
            }
          ]
        },
        {
          "cohort": "cSCC_scRNAseq_visium",
          "donorNumber": 10,
          "preferredDataset": []
        }
      ]
    },
    
    {
      "study": "10x Visium Mouse Sagittal Anterior1",
      "label": "Mouse Sagittal Anterior (10x Visium demo dataset), integrated with Allen Brain scRNA-seq",
      "publication": [
        {
          "title": "Mouse Brain Serial Section 1 (Sagittal-Anterior)",
          "abstract": "10x Genomics obtained fresh frozen mouse brain tissue (Strain C57BL/6) from BioIVT Asterand. The tissue was embedded and cryosectioned as described in Visium Spatial Protocols - Tissue Preparation Guide Demonstrated Protocol (CG000240). Tissue sections of 10 µm thickness from a sagittal slice of the anterior were placed on Visium Gene Expression Slides.",
          "url": "https://www.10xgenomics.com/resources/datasets/mouse-brain-serial-section-1-sagittal-anterior-1-standard-1-1-0"
        }
      ],
      "cohortList": [
        {
          "cohort": "10x visium Mouse Sagittal Anterior1",
          "donorNumber": 1,
          "preferredDataset": [
            {
              "host": "https://previewsinglecell.xenahubs.net",
              "name": "visium_Mouse_Brain_Sagittal_Anterior1/exprMatrix.tsv"
            }
          ]
        },
        {
          "cohort": "Integration visium Mouse Sagittal Anterior1 + Allen Brain visual cortex"
        }
      ]
    },
    {
      "study": "Goltsev_CODEX",
      "label": "Deep Profiling of Mouse Splenic Architecture with CODEX Multiplexed Imaging (Goltsev et al Cell 2018)",
      "publication": [
        {
          "title": "Deep Profiling of Mouse Splenic Architecture with CODEX Multiplexed Imaging",
          "abstract": "A highly multiplexed cytometric imaging approach, termed co-detection by indexing (CODEX), is used here to create multiplexed datasets of normal and lupus (MRL/lpr) murine spleens. CODEX iteratively visualizes antibody binding events using DNA barcodes, fluorescent dNTP analogs, and an in situ polymerization-based indexing procedure. An algorithmic pipeline for single-cell antigen quantification in tightly packed tissues was developed and used to overlay well-known morphological features with de novo characterization of lymphoid tissue architecture at a single-cell and cellular neighborhood levels. We observed an unexpected, profound impact of the cellular neighborhood on the expression of protein receptors on immune cells. By comparing normal murine spleen to spleens from animals with systemic autoimmune disease (MRL/lpr), extensive and previously uncharacterized splenic cell-interaction dynamics in the healthy versus diseased state was observed. The fidelity of multiplexed spatial cytometry demonstrated here allows for quantitative systemic characterization of tissue architecture in normal and clinically aberrant samples.",
          "url": "https://doi.org/10.1016/j.cell.2018.07.010, https://data.mendeley.com/datasets/zjnpwh8m5b/1"
        }
      ],
      "cohortList": [
        {
          "cohort": "Goltsev_CODEX",
          "donorNumber": 3,
          "preferredDataset": [
            {
              "host": "https://previewsinglecell.xenahubs.net",
              "name": "Goltsev_CODEX/codex_balbc1.protein.tsv"
            }
          ]
        }
      ]
    },
    
    {
      "study": "Govek_CITEseq",
      "label": "Single-cell transcriptomic analysis of mIHC images via antigen mapping (Govek et al Science Adv 2021)",
      "publication": [
        {
          "title": "Single-cell transcriptomic analysis of mIHC images via antigen mapping",
          "abstract": "Highly multiplexed immunohistochemistry (mIHC) enables the staining and quantification of dozens of antigens in a tissue section with single-cell resolution. However, annotating cell populations that differ little in the profiled antigens or for which the antibody panel does not include specific markers is challenging. To overcome this obstacle, we have developed an approach for enriching mIHC images with single-cell RNA sequencing data, building upon recent experimental procedures for augmenting single-cell transcriptomes with concurrent antigen measurements. Spatially-resolved Transcriptomics via Epitope Anchoring (STvEA) performs transcriptome-guided annotation of highly multiplexed cytometry datasets. It increases the level of detail in histological analyses by enabling the systematic annotation of nuanced cell populations, spatial patterns of transcription, and interactions between cell types. We demonstrate the utility of STvEA by uncovering the architecture of poorly characterized cell types in the murine spleen using published cytometry and mIHC data of this organ.",
          "url": "https://pubmed.ncbi.nlm.nih.gov/33674303/, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE160766"
        }
      ],
      "cohortList": [
        {
          "cohort": "Govek_CITEseq",
          "donorNumber": 5,
          "preferredDataset": [
            {
              "host": "https://previewsinglecell.xenahubs.net",
              "name": "Govek_CITEseq/citeseq_ADT_log2.tsv"
            },
            {
              "host": "https://previewsinglecell.xenahubs.net",
              "name": "Govek_CITEseq/citeseq_ADT.tsv"
            },
            {
              "host": "https://previewsinglecell.xenahubs.net",
              "name": "Govek_CITEseq/citeseq_mRNA.tsv"
            }
          ]
        }
      ]
    },
    
    {
      "study": "follicular_remodeling",
      "label": "Single-cell reconstruction of follicular remodeling in the human adult ovary (Fan et al Nat Commun 2019)",
      "publication": [
        {
          "title": "Single-cell reconstruction of follicular remodeling in the human adult ovary",
          "abstract": "The ovary is perhaps the most dynamic organ in the human body, only rivaled by the uterus. The molecular mechanisms that regulate follicular growth and regression, ensuring ovarian tissue homeostasis, remain elusive. We have performed single-cell RNA-sequencing using human adult ovaries to provide a map of the molecular signature of growing and regressing follicular populations. We have identified different types of granulosa and theca cells and detected local production of components of the complement system by (atretic) theca cells and stromal cells. We also have detected a mixture of adaptive and innate immune cells, as well as several types of endothelial and smooth muscle cells to aid the remodeling process. Our results highlight the relevance of mapping whole adult organs at the single-cell level and reflect ongoing efforts to map the human body. The association between complement system and follicular remodeling may provide key insights in reproductive biology and (in)fertility.",
          "url": "https://www.nature.com/articles/s41467-019-11036-9, https://cellxgene.cziscience.com/collections/2902f08c-f83c-470e-a541-e463e25e5058"
        }
      ],
      "cohortList": [
        {
          "cohort": "follicular_remodeling",
          "donorNumber": 5,
          "preferredDataset": [
            {
              "host": "https://previewsinglecell.xenahubs.net",
              "name": "follicular_remodeling/exprMatrix.tsv"
            }
          ]
        }
      ]
    },
    
    {
      "study": "Melanoma_immunotherapy_resistance",
      "label": "Melanoma immunotherapy resistance (Jerby-Arnon et al Cell 2018)",
      "publication": [
        {
          "title": "A Cancer Cell Program Promotes T Cell Exclusion and Resistance to Checkpoint Blockade",
          "abstract": "mmune checkpoint inhibitors (ICIs) produce durable responses in some melanoma patients, but many patients derive no clinical benefit, and the molecular underpinnings of such resistance remain elusive. Here, we leveraged single-cell RNA sequencing (scRNA-seq) from 33 melanoma tumors and computational analyses to interrogate malignant cell states that promote immune evasion. We identified a resistance program expressed by malignant cells that is associated with T cell exclusion and immune evasion. The program is expressed prior to immunotherapy, characterizes cold niches in situ, and predicts clinical responses to anti-PD-1 therapy in an independent cohort of 112 melanoma patients. CDK4/6-inhibition represses this program in individual malignant cells, induces senescence, and reduces melanoma tumor outgrowth in mouse models in vivo when given in combination with immunotherapy. Our study provides a high-resolution landscape of ICI-resistant cell states, identifies clinically predictive signatures, and suggests new therapeutic strategies to overcome immunotherapy resistance.",
          "url": "https://www.cell.com/cell/fulltext/S0092-8674(18)31178-4"
        }
      ],
      "cohortList": [
        {
          "cohort": "Melanoma scRNA-seq (Jerby-Arnon cell 2018)",
          "donorNumber": 31,
          "preferredDataset": [
            {
              "host": "https://previewsinglecell.xenahubs.net",
              "name": "melanoma_Jerby-Arnon/tumors_tpm.txt"
            }
          ]
        }
      ]
    },
    
    {
      "study": "Therapy-Induced_Longitudinal_Lung_Cancer_Bivona_2020",
      "label": "Therapy-Induced Evolution of Human Lung Cancer Revealed by Single-Cell RNA Sequencing (Maynard et al Cell 2020)",
      "publication": [
        {
          "title": "Therapy-Induced Evolution of Human Lung Cancer Revealed by Single-Cell RNA Sequencing",
          "abstract": "Lung cancer, the leading cause of cancer mortality, exhibits heterogeneity that enables adaptability, limits therapeutic success, and remains incompletely understood. Single-cell RNA sequencing (scRNA-seq) of metastatic lung cancer was performed using 49 clinical biopsies obtained from 30 patients before and during targeted therapy. Over 20,000 cancer and tumor microenvironment (TME) single-cell profiles exposed a rich and dynamic tumor ecosystem. scRNA-seq of cancer cells illuminated targetable oncogenes beyond those detected clinically. Cancer cells surviving therapy as residual disease (RD) expressed an alveolar-regenerative cell signature suggesting a therapy-induced primitive cell-state transition, whereas those present at on-therapy progressive disease (PD) upregulated kynurenine, plasminogen, and gap-junction pathways. Active T-lymphocytes and decreased macrophages were present at RD and immunosuppressive cell states characterized PD. Biological features revealed by scRNA-seq were biomarkers of clinical outcomes in independent cohorts. This study highlights how therapy-induced adaptation of the multi-cellular ecosystem of metastatic cancer shapes clinical outcomes.",
          "url": "https://pubmed.ncbi.nlm.nih.gov/32822576"
        }
      ],
      "cohortList": [
        {
          "cohort": "Longitudinal Lung Cancer scRNA (Bivona 2020)",
          "donorNumber": 33,
          "preferredDataset": [
            {
              "host": "https://previewsinglecell.xenahubs.net",
              "name": "lungCancerBivona/S03_Main_Seurat_object_filtered_and_subset_annotated_scaledata.tsv"
            }
          ]
        }
      ]
    },
    
    {
      "study": "zebrahub",
      "label": "Zebrahub",
      "publication": [
        {
          "title": "Zebrahub sequencing and imaging atlas of zebrafish development",
          "abstract": "Zebrahub is a comprehensive atlas of zebrafish embryonic development that combines single-cell RNA sequencing and live light-sheet imaging. Its aim is to provide a complete cartography of cellular lineages in space, time, and molecular domains, an essential step toward understanding how organisms develop.<br><br>Our ultimate goal is to create a high-quality multimodal foundational resource of vertebrate development and make it broadly available to the scientific community.",
          "url": "https://zebrahub.ds.czbiohub.org/"
        }
      ],
      "cohortList": [
        {
          "cohort": "zebrahub",
          "donorNumber": 40,
          "preferredDataset": [
            {
              "host": "https://previewsinglecell.xenahubs.net",
              "name": "zebrahub/exprMatrix.tsv"
            }
          ]
        }
      ]
    },
    
    {
      "study": "Goltsev_Govek",
      "label": "Mouse spleen",
      "subStudy": [
        {
          "studyID": "Govek_CITEseq",
          "displayLabel": "Govek Science Adv 2021"
        },
        {
          "studyID": "Goltsev_CODEX",
          "displayLabel": "Goltsev Cell 2018"
        }
      ]
    },
    
    {
      "study": "AtlasMelanoma_Jerby-Arnon",
      "label": "Melanoma",
      "subStudy": [
        {
          "studyID": "Atlas_melanoma_MEL08",
          "displayLabel": "HTAN Melanoma ATLAS (Ajit et al Cancer Discovery 2022)"
        },
        {
          "studyID": "Melanoma_immunotherapy_resistance",
          "displayLabel": "Melanoma immunotherapy resistance (Jerby-Arnon cell 2018)"
        }
      ],
      "cohortList": [
        {
          "cohort": "Integration melanoma (scRNA-seq, t-CycIF)"
        }
      ]
    }
  ]
}