The inflammasome of circulatory collapse: single cell analysis of survival on extracorporeal life support

Kort, Eric J.; Weiland, Matthew; Grins, Edgars; Eugster, Emily; Milliron, Hsiao-yun; Kelty, C.E.; Nm, Shrestha; Timek, Tomasz A.; Leacche, Marzia; Sj, Fitch; Tj, Boeve; Marco, G.; Dickinson, Michael G.; Wilton, P.; Jovinge, Stefan

doi:10.1101/568659

Cited by 3 publications

(5 citation statements)

References 46 publications

(73 reference statements)

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“…The finding of changes in the neutrophil count was independently validated using additional single cell RNA-seq data of ECMO adult patients data [11], where we observed decrease of expression of neutrophil gene markers and genes involved in inflammatory response in deceased ECMO patients compared to patients that survived ( Figure S7 and S8). Further, paired comparison of neutrophil levels for each patient showed no significant change across different time points ( Fig.…”

Section: Immune Cells Deconvolution and Transcriptome Analysissupporting

confidence: 54%

“…The MODS patients in this validation dataset were those patients, who require intensive care support (ICU) for their survival (similar to our ECMO patients) and those do not need ICU support were labelled as "noMODS" (similar to our MODS patients) as described in patient demographics [10]. In addition, a single cell RNA-Seq dataset was also available for adult ECMO patients [11]. We used the immune cell markers from this dataset to validate our immune response analysis.…”

Section: Validation Datasetsmentioning

confidence: 99%

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Gene expression signatures identify paediatric patients with multiple organ dysfunction who require advanced life support in the intensive care unit

et al. 2020

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Background: Multiple organ dysfunction syndrome (MODS) occurs in the setting of a variety of pathologies including infection and trauma. Some patients decompensate and require Veno-Arterial extra corporeal membrane oxygenation (ECMO) as a palliating manoeuvre for recovery of cardiopulmonary function. The molecular mechanisms driving progression from MODS to cardiopulmonary collapse remain incompletely understood, and no biomarkers have been defined to identify those MODS patients at highest risk for progression to requiring ECMO support. Methods: Whole blood RNA-seq profiling was performed for 23 MODS patients at three time points during their ICU stay (at diagnosis of MODS, 72 hours after, and 8 days later), as well as four healthy controls undergoing routine sedation. Of the 23 MODS patients, six required ECMO support (ECMO patients). The predictive power of conventional demographic and clinical features was quantified for differentiating the MODS and ECMO patients. We then compared the performance of markers derived from transcriptomic profiling including [1] transcriptomically imputed leukocyte subtype distribution, [2] relevant published gene signatures and [3] a novel differential gene expression signature computed from our data set. The predictive power of our novel gene expression signature was then validated using independently published datasets. Finding: None of the five demographic characteristics and 14 clinical features, including The Paediatric Logistic Organ Dysfunction (PELOD) score, could predict deterioration of MODS to ECMO at baseline. From previously published sepsis signatures, only the signatures positively associated with patient's mortality could differentiate ECMO patients from MODS patients, when applied to our transcriptomic dataset (P-value ranges from 0.01 to 0.04, Student's test). Deconvolution of bulk RNA-Seq samples suggested that lower neutrophil counts were associated with increased risk of progression from MODS to ECMO (P-value = 0.03, logistic regression, OR=2.82 [95% CI 0.63-12.45]). A total of 30 genes were differentially expressed between ECMO and MODS patients at baseline (log2 fold change 1 or-1 with false discovery rate 0.01). These genes are involved in protein maintenance and epigenetic-related processes. Further univariate analysis of these 30 genes suggested a signature of seven DE genes associated with ECMO (OR > 3.0, P-value 0.05, logistic regression). Notably, this contains a set of histone marker genes, including H1F0, HIST2H3C, HIST1H2AI, HIST1H4, HIST1H2BL and HIST1H1B, that were highly expressed in ECMO. A risk score derived from expression of these genes differentiated ECMO and MODS patients in our dataset (AUC = 0.91, 95% CI 0.79-1.00, Pvalue = 7e-04, logistic regression) as well as validation dataset (AUC= 0.73, 95% CI 0.53-0.93, P-value = 2e-02, logistic regression).

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Section: Immune Cells Deconvolution and Transcriptome Analysissupporting

confidence: 54%

Section: Validation Datasetsmentioning

confidence: 99%

Gene expression signatures identify paediatric patients with multiple organ dysfunction who require advanced life support in the intensive care unit

et al. 2020

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“…The finding of changes in the neutrophil count was independently validated using additional single cell RNA-seq data of ECMO adult patients data [8], where we observed decrease of expression of neutrophil gene markers and genes involved in inflammatory response in deceased ECMO patients compared to patients that survived ( Figure S7 and S8). Further paired comparison of neutrophil levels for each patient showed no significant change across different time points ( Fig.…”

Section: Immune Cells Deconvolution and Transcriptome Analysissupporting

confidence: 54%

“…The MODS patients in this validation dataset were categorized into MODS and noMODS (patients did not develop MODS) as described in patient demographics [7]. In addition, a single cell RNA-Seq dataset was also available for adult ECMO patients [8]. We used the immune cell markers from this dataset to validate our immune response analysis.…”

Section: Validation Data Setsmentioning

confidence: 99%

Gene expression signatures identify pediatric patients with multiple organ dysfunction who require advanced life support in the intensive care unit

Shankar

Leimanis

Newbury

et al. 2020

Preprint

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Background: Multiple organ dysfunction syndrome (MODS) occurs in the setting of a variety of pathologies including infection and trauma. Some of these patients will further decompensate and require extra corporeal membrane oxygenation (ECMO) as a palliating maneuver to allow time for recovery of cardiopulmonary function. The molecular mechanisms driving progression from MODS to cardiopulmonary collapse remain incompletely understood, and no biomarkers have been defined to identify those MODS patients at highest risk for progression to requiring ECMO support. We hypothesize that molecular features derived from whole blood transcriptomic profiling either alone or in combination with traditional clinical and laboratory markers can prospectively identify these high risk MODS patients in the pediatric intensive care unit (PICU). Design/Methods: Whole blood RNA-seq profiling was performed for 23 MODS patients at three time points during their ICU stay (at diagnosis of MODS, 72 hours after, and 8 days later), as well as four healthy controls undergoing routine sedation. Of the 23 MODS patients, six required ECMO support (ECMO patients). The predictive power of conventional demographic and clinical features was quantified for differentiating the MODS and ECMO patients. We then compared the performance of markers derived from transcriptomic profiling including (1) transcriptomically imputed leukocyte subtype distribution, (2) relevant published gene signatures and (3) a novel differential gene expression signature computed from our data set. The predictive power of our novel gene expression signature was then validated using independently published datasets. Results: None of the five demographic characteristics and 14 clinical features, including The Pediatric Logistic Organ Dysfunction (PELOD) score, could predict deterioration of MODS to ECMO at baseline. From previously published sepsis signatures, only the signatures positively associated with patients mortality could differentiate ECMO patients from MODS patients, when applied to our transcriptomic dataset (P-value ranges from 0.01 to 0.04). Deconvolution of bulk RNA-Seq samples suggested that lower neutrophil counts were associated with increased risk of progression from MODS to ECMO (P-value = 0.03, OR=2.82 [95% CI 0.63 - 12.45]). A total of 28 genes were differentially expressed between ECMO and MODS patients at baseline (log2 fold change ≥ 1 or ≤ -1 with false discovery rate ≤ 0.2). These genes are involved in protein maintenance and epigenetic-related processes. Further univariate analysis of these 28 genes suggested a signature of six DE genes associated with ECMO (OR > 3.0, P-value ≤ 0.05). Notably, this contains a set of histone marker genes, including H1F0, HIST2H3C, HIST1H2AI, HIST1H4, and HIST1H1B, that were highly expressed in ECMO. A risk score derived from expression of these genes differentiated ECMO and MODS patients in our dataset (AUC = 0.91, 95% CI 0.79-0.1.00, P-value = 7e-04) as well as validation dataset (AUC= 0.73,95% CI 0.53-0.93, P-value = 2e-02). Conclusions: This study identified lower neutrophils and upregulation of specific histone related genes as a putative signature for deterioration of MODS to ECMO. This study demonstrates that transcriptomic features may be superior to traditional clinical methods of ascertaining severity in patients with MODS.

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“…We barcoded AAV serotypes according to a new design principle, applied this AAV library on complex mixtures of cell types, conducted single-cell sequencing 19,20,21,22,23,24 to identify both the cell type and the AAV barcodes the single cell contains, and deconvoluted these data into matrices of AAV serotype versus human cell types. We applied this technology in human organoids, which can recapitulate certain structural and cellular complexity of the human brain and eye, and identified how efficiently and specifically each AAV serotype transduces individual cell types found within the organoids.…”

Section: Introductionmentioning

confidence: 99%

Multiplex viral tropism assay in complex cell populations with single-cell resolution

Keng

Guo

Lim

et al. 2021

Preprint

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Gene therapy constitutes one of the most promising modes of disease treatments. Two key properties for therapeutic delivery vectors are the transduction efficiency (how well the vector delivers therapeutic cargo to desired target cells) and specificity (how well it avoids off-target delivery into the other unintended cells within the body). Here we developed a novel technology that enables multiplex measurement of transduction efficiency and specificity, particularly by measuring how libraries of delivery vectors transduce libraries of diverse cell types. We demonstrated that pairing high-throughput measurement of AAV identity with high-resolution single-cell RNA transcriptomic sequencing maps how natural and engineered AAV variants transduce individual cells within human cerebral and ocular organoids. This library-on-library technology is important for determining the safety and efficacy of therapeutic delivery vectors.

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The inflammasome of circulatory collapse: single cell analysis of survival on extracorporeal life support

Cited by 3 publications

References 46 publications

Gene expression signatures identify paediatric patients with multiple organ dysfunction who require advanced life support in the intensive care unit

Gene expression signatures identify paediatric patients with multiple organ dysfunction who require advanced life support in the intensive care unit

Gene expression signatures identify pediatric patients with multiple organ dysfunction who require advanced life support in the intensive care unit

Multiplex viral tropism assay in complex cell populations with single-cell resolution

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