SpatialCells: automated profiling of tumor microenvironments with spatially resolved multiplexed single-cell data.

Cancer is a complex cellular ecosystem where malignant cells coexist and interact with immune, stromal and other cells within the tumor microenvironment (TME). Recent technological advancements in spatially resolved multiplexed imaging at single-cell resolution have led to the generation of large-scale and high-dimensional datasets from biological specimens. This underscores the necessity for automated methodologies that can effectively characterize molecular, cellular and spatial properties of TMEs for various malignancies. This study introduces SpatialCells, an open-source software package designed for region-based exploratory analysis and comprehensive characterization of TMEs using multiplexed single-cell data. The source code and tutorials are available at https://semenovlab.github.io/SpatialCells. SpatialCells efficiently streamlines the automated extraction of features from multiplexed single-cell data and can process samples containing millions of cells. Thus, SpatialCells facilitates subsequent association analyses and machine learning predictions, making it an essential tool in advancing our understanding of tumor growth, invasion and metastasis.

The role of intracranial pressure variability as a predictor of intracranial hypertension and mortality in critically ill patients.

OBJECTIVE: Intracranial pressure (ICP) monitoring is a widely utilized and essential tool for tracking neurosurgical patients, but there are limitations to the use of a solely ICP-based paradigm for guiding management. It has been suggested that ICP variability (ICPV), in addition to mean ICP, may be a useful predictor of neurological outcomes, as it represents an indirect measure of intact cerebral pressure autoregulation. However, the current literature regarding the applicability of ICPV shows conflicting associations between ICPV and mortality. Thus, the authors aimed to investigate the effect of ICPV on intracranial hypertensive episodes and mortality using the eICU Collaborative Research Database version 2.0. METHODS: The authors extracted from the eICU database 1,815,676 ICP readings from 868 patients with neurosurgical conditions. ICPV was computed using two methods: the rolling standard deviation (RSD) and the absolute deviation from the rolling mean (DRM). An episode of intracranial hypertension was defined as at least 25 minutes of ICP > 22 mm Hg in any 30-minute window. The effects of mean ICPV on intracranial hypertension and mortality were computed using multivariate logistic regression. A recurrent neural network with long short-term memory was used for time-series predictions of ICP and ICPV to prognosticate future episodes of intracranial hypertension. RESULTS: A higher mean ICPV was significantly associated with intracranial hypertension using both ICPV definitions (RSD: aOR 2.82, 95% CI 2.07-3.90, p < 0.001; DRM: aOR 3.93, 95% CI 2.77-5.69, p < 0.001). ICPV was significantly associated with mortality in patients with intracranial hypertension (RSD: aOR 1.28, 95% CI 1.04-1.61, p = 0.026, DRM: aOR 1.39, 95% CI 1.10-1.79, p = 0.007). In the machine learning models, both definitions of ICPV achieved similarly good results, with the best F1 score of 0.685 ± 0.026 and an area under the curve of 0.980 ± 0.003 achieved with the DRM definition over 20 minutes. CONCLUSIONS: ICPV may be useful as an adjunct for the prognostication of intracranial hypertensive episodes and mortality in neurosurgical critical care as part of neuromonitoring. Further research on predicting future intracranial hypertensive episodes with ICPV may help clinicians react expediently to ICP changes in patients.

SpatialCells: Automated Profiling of Tumor Microenvironments with Spatially Resolved Multiplexed Single-Cell Data.

Background: Cancer is a complex cellular ecosystem where malignant cells coexist and interact with immune, stromal, and other cells within the tumor microenvironment. Recent technological advancements in spatially resolved multiplexed imaging at single-cell resolution have led to the generation of large-scale and high-dimensional datasets from biological specimens. This underscores the necessity for automated methodologies that can effectively characterize the molecular, cellular, and spatial properties of tumor microenvironments for various malignancies. Results: This study introduces SpatialCells, an open-source software package designed for region-based exploratory analysis and comprehensive characterization of tumor microenvironments using multiplexed single-cell data. Conclusions: SpatialCells efficiently streamlines the automated extraction of features from multiplexed single-cell data and can process samples containing millions of cells. Thus, SpatialCells facilitates subsequent association analyses and machine learning predictions, making it an essential tool in advancing our understanding of tumor growth, invasion, and metastasis.