Single-cell analysis of severe COVID-19 patients reveals a monocyte-driven inflammatory storm attenuated by Tocilizumab

ABSTRACTDespite the current devastation of the COVID-19 pandemic, several recent studies have suggested that the immunosuppressive drug Tocilizumab can powerfully treating inflammatory responses that occur in this disease. Here, by employing single-cell analysis of the immune cell composition of severe-stage COVID-19 patients and these same patients in post Tocilizumab-treatment remission, we have identified a monocyte subpopulation specific to severe disease that contributes to inflammatory storms in COVID-19 patients. Although Tocilizumab treatment attenuated the strong inflammatory immune response, we found that immune cells including plasma B cells and CD8+ T cells still exhibited an intense humoral and cell-mediated anti-virus immune response in COVID-19 patients after Tocilizumab treatment. Thus, in addition to providing a rich, very high-resolution data resource about the immune cell distribution at multiple stages of the COVID-19 disease, our work both helps explain Tocilizumab’s powerful therapeutic effects and defines a large number of potential new drug targets related to inflammatory storms.

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Decision letter: Single cell analysis reveals immune cell–adipocyte crosstalk regulating the transcription of thermogenic adipocytes

10.7554/elife.49501.020 ◽

2019 ◽

Author(s):

Philipp E Scherer

Keyword(s):

Single Cell ◽

Immune Cell ◽

Single Cell Analysis ◽

Cell Analysis ◽

Thermogenic Adipocytes

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Stratification of chemotherapy-treated stage III colorectal cancer patients using multiplexed imaging and single-cell analysis of T-cell populations

Modern Pathology ◽

10.1038/s41379-021-00953-0 ◽

2021 ◽

Author(s):

Xanthi Stachtea ◽

Maurice B. Loughrey ◽

Manuela Salvucci ◽

Andreas U. Lindner ◽

Sanghee Cho ◽

...

Keyword(s):

Colorectal Cancer ◽

Overall Survival ◽

Single Cell ◽

Immune Cell ◽

Single Cell Analysis ◽

Cell Types ◽

Cell Segmentation ◽

Stage Iii ◽

Cell Analysis ◽

Increased Risk

AbstractColorectal cancer (CRC) has one of the highest cancer incidences and mortality rates. In stage III, postoperative chemotherapy benefits <20% of patients, while more than 50% will develop distant metastases. Biomarkers for identification of patients at increased risk of disease recurrence following adjuvant chemotherapy are currently lacking. In this study, we assessed immune signatures in the tumor and tumor microenvironment (TME) using an in situ multiplexed immunofluorescence imaging and single-cell analysis technology (Cell DIVETM) and evaluated their correlations with patient outcomes. Tissue microarrays (TMAs) with up to three 1 mm diameter cores per patient were prepared from 117 stage III CRC patients treated with adjuvant fluoropyrimidine/oxaliplatin (FOLFOX) chemotherapy. Single sections underwent multiplexed immunofluorescence staining for immune cell markers (CD45, CD3, CD4, CD8, FOXP3, PD1) and tumor/cell segmentation markers (DAPI, pan-cytokeratin, AE1, NaKATPase, and S6). We used annotations and a probabilistic classification algorithm to build statistical models of immune cell types. Images were also qualitatively assessed independently by a Pathologist as ‘high’, ‘moderate’ or ‘low’, for stromal and total immune cell content. Excellent agreement was found between manual assessment and total automated scores (p < 0.0001). Moreover, compared to single markers, a multi-marker classification of regulatory T cells (Tregs: CD3+/CD4+FOXP3+/PD1−) was significantly associated with disease-free survival (DFS) and overall survival (OS) (p = 0.049 and 0.032) of FOLFOX-treated patients. Our results also showed that PD1− Tregs rather than PD1+ Tregs were associated with improved survival. These findings were supported by results from an independent FOLFOX-treated cohort of 191 stage III CRC patients, where higher PD1− Tregs were associated with an increase overall survival (p = 0.015) for CD3+/CD4+/FOXP3+/PD1−. Overall, compared to single markers, multi-marker classification provided more accurate quantitation of immune cell types with stronger correlations with outcomes.

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Systems-level immunomonitoring from acute to recovery phase of severe COVID-19

10.1101/2020.06.03.20121582 ◽

2020 ◽

Cited By ~ 7

Author(s):

Lucie Rodriguez ◽

Pirkka Pekkarinen ◽

Tadepally Lakshmikanth ◽

Ziyang Tan ◽

Camila Rosat Consiglio ◽

...

Keyword(s):

Immune Response ◽

Immune Cell ◽

Inflammatory Responses ◽

Severe Disease ◽

Recovery Phase ◽

Sudden Onset ◽

The Many ◽

Humoral Responses ◽

Disease Stages ◽

Cell Cell

SUMMARYThe immune response to SARS-CoV2 is under intense investigation, but not fully understood att this moment. Severe disease is characterized by vigorous inflammatory responses in the lung, often with a sudden onset after 5–7 days of stable disease. Efforts to modulate this hyperinflammation and the associated acute respiratory distress syndrome, rely on the unraveling of the immune cell interactions and cytokines that drive such responses. Systems-level analyses are required to simultaneously capture all immune cell populations and the many protein mediators by which cells communicate. Since every patient analyzed will be captured at different stages of his or her infection, longitudinal monitoring of the immune response is critical. Here we report on a systems-level blood immunomonitoring study of 39 adult patients, hospitalized with severe COVID-19 and followed with up to 14 blood samples from acute to recovery phases of the disease. We describe an IFNγ – Eosinophil axis activated prior to lung hyperinflammation and changes in cell-cell coregulation during different stages of the disease. We also map an immune trajectory during recovery that is shared among patients with severe COVID-19.HIGHLIGHTSSystems-level immunomonitoring from acute to recovery in severe COVID-19An IFNγ - Eosinophil axis involved in lung hyperinflammationCell-cell coregulation differ during four disease stagesBasophils and hyperinflammation modulate humoral responsesA shared trajectory of immunological recovery in severe COVID-19

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High dimensional single cell analysis reveals unexpected immune cell types, and loss of motility of alveolar macrophages regulated by PPARγ in chronic obstructive pulmonary disease

10.1055/s-0039-1678319 ◽

2019 ◽

Author(s):

W Fujii ◽

K Baßler ◽

T Kapellos ◽

AC Aschenbrenner ◽

K Händler ◽

...

Keyword(s):

Chronic Obstructive Pulmonary Disease ◽

Single Cell ◽

Alveolar Macrophages ◽

Immune Cell ◽

Single Cell Analysis ◽

Cell Types ◽

High Dimensional ◽

Chronic Obstructive ◽

Cell Analysis ◽

Obstructive Pulmonary Disease

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Abstract IA04: The immune cell microenvironment of classical Hodgkin lymphoma by single-cell analysis

10.1158/2643-3249.lymphoma20-ia04 ◽

2020 ◽

Author(s):

Christian Steidl

Keyword(s):

Single Cell ◽

Hodgkin Lymphoma ◽

Immune Cell ◽

Single Cell Analysis ◽

Cell Analysis ◽

Classical Hodgkin Lymphoma ◽

Cell Microenvironment

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Deciphering Tumor Heterogeneity in Hepatocellular Carcinoma (HCC)—Multi-Omic and Singulomic Approaches

Seminars in Liver Disease ◽

10.1055/s-0040-1722261 ◽

2021 ◽

Author(s):

Renumathy Dhanasekaran

Keyword(s):

Single Cell ◽

Tumor Heterogeneity ◽

Immune Cell ◽

Single Cell Analysis ◽

Sampling Strategies ◽

Cell Analysis ◽

Single Cell Sequencing ◽

Sequencing Technologies ◽

Hepatocellular Carcinomas ◽

Generation Sequencing

AbstractTumor heterogeneity, a key hallmark of hepatocellular carcinomas (HCCs), poses a significant challenge to developing effective therapies or predicting clinical outcomes in HCC. Recent advances in next-generation sequencing-based multi-omic and single cell analysis technologies have enabled us to develop high-resolution atlases of tumors and pull back the curtain on tumor heterogeneity. By combining multiregion targeting sampling strategies with deep sequencing of the genome, transcriptome, epigenome, and proteome, several studies have revealed novel mechanistic insights into tumor initiation and progression in HCC. Advances in multiparametric immune cell profiling have facilitated a deeper dive into the biological complexity of HCC, which is crucial in this era of immunotherapy. Moreover, studies using liquid biopsy have demonstrated their potential to circumvent the need for tissue sampling to investigate heterogeneity. In this review, we discuss how multi-omic and single-cell sequencing technologies have advanced our understanding of tumor heterogeneity in HCC.

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Stratification of Chemotherapy-Treated Stage III Colorectal Cancer Patients Using Multiplexed Imaging and Single Cell Analysis of T Cell Populations

10.1101/2021.02.24.432210 ◽

2021 ◽

Author(s):

Xanthi Stachtea ◽

Maurice B. Loughrey ◽

Manuela Salvucci ◽

Andreas U. Lindner ◽

Sanghee Cho ◽

...

Keyword(s):

Colorectal Cancer ◽

Single Cell ◽

Immune Cells ◽

Immune Cell ◽

Single Cell Analysis ◽

Cell Segmentation ◽

Stage Iii ◽

Support Vector ◽

Cell Analysis ◽

Cell Markers

AbstractColorectal cancer (CRC) has one of the highest cancer incidences and mortality rates. In stage III, postoperative chemotherapy benefits <20% of patients, while more than 50% will develop distant metastases. Predictive biomarkers for identification of patients with increased risk for disease recurrence are currently lacking, with progress in biomarker discovery hindered by the disease’s inherent heterogeneity. The immune profile of colorectal tumors has previously been found to have prognostic value. The aims of this study were to evaluate immune signatures in the tumor microenvironment (TME) using an in situ multiplexed immunofluorescence imaging and single cell analysis technology (Cell DIVE™). Tissue microarrays (TMAs) with up to three 1mm diameter cores per patient were prepared from 117 stage III CRC patients treated with adjuvant fluoropyrimidine/oxaliplatin chemotherapy. Single sections underwent multilplexed immunofluorescence with Cy3- and Cy5-conjugated antibodies for immune cell markers (CD45, CD3, CD4, CD8, FOXP3, PD1) and cell segmentation markers (DAPI, pan-cytokeratin, AE1, NaKATPase and S6). We applied a probabilistic multi-class, multi-label classification algorithm based on multi-parametric models to build statistical models of protein expression to classify immune cells. Expert annotations of immune cell markers were made on a range of images, and Support Vector Machines (SVM) were used to derive a statistical model for cell classification. Images were also manually scored independently by a Pathologist as ‘high’, ‘moderate’ or ‘low’, for stromal and total immune cell content. Excellent agreement was found between manual and total automated scores (p<0.0001). Higher levels of multi-marker classified regulatory T cells (CD3+CD4+FOXP3+PD1-) were significantly associated with disease-free survival (DFS) and overall-survival (OS) (p=0.049 and 0.032), compared to FOXP3 alone. Our results also showed that PD1- Tregs rather than PD1+ Tregs were associated with improved survival. Overall, compared to single markers, multi-marker classification provided more accurate quantitation of immune cells with greater potential for predicting patient outcomes.

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