Biology subjects

Rickert, C.

Publications and source records attributed to Rickert, C..

3 recordsLinked to original sources

bioRxiv2024

The spatial structure of the tumor immune microenvironment can explain and predict patient response in high-grade serous carcinoma

Despite ovarian cancer being the deadliest gynecological malignancy, there has been little change to therapeutic options and mortality rates over the last three decades. Recent studies indicate that the composition of the tumor immune microenvironment (TIME) influences patient outcomes but are limited by a lack of spatial understanding. We performed multiplexed ion beam imaging (MIBI) on 83 human high-grade serous carcinoma tumors -- one of the largest protein-based, spatially-intact, single-cell resolution tumor datasets assembled -- and used statistical and machine learning approaches to connect features of the TIME spatial organization to patient outcomes. Along with traditional clinical/immunohistochemical attributes and indicators of TIME composition, we found that several features of TIME spatial organization had significant univariate correlations and/or high relative importance in high-dimensional predictive models. The top performing predictive model for patient progression-free survival (PFS) used a combination of TIME composition and spatial features. Results demonstrate the importance of spatial structure in understanding how the TIME contributes to treatment outcomes. Furthermore, the present study provides a generalizable roadmap for spatial analyses of the TIME in ovarian cancer research.

cancer biology↗

bioRxiv2022

A Platform-Independent Framework for Phenotyping of Multiplex Tissue Imaging Data

Multiplex imaging is a powerful tool to analyze the structural and functional states of cells in their morphological and pathological contexts. However, hypothesis testing with multiplex imaging data is a challenging task due to the extent and complexity of the information obtained. Various computational pipelines have been developed and validated to extract knowledge from specific imaging platforms. A common problem with customized pipelines is their reduced applicability across different imaging platforms: Every multiplex imaging technique exhibits platform-specific characteristics in terms of signal-to-noise ratio and acquisition artifacts that need to be accounted for to yield reliable and reproducible results. We propose a pixel classifier-based image preprocessing step that aims to minimize platform-dependency for all multiplex image analysis pipelines. Signal detection and noise reduction as well as artifact removal can be posed as a pixel classification problem in which all pixels in multiplex images can be assigned to two general classes of either I) signal of interest or II) artifacts and noise. The resulting feature representation maps contain pixel-accurate representations of the input data, but exhibit signifi-cantly increased signal-to-noise ratios with normalized pixel values as output data. We demonstrate the validity of our proposed image preprocessing approach by comparing the results of two well-accepted and widely-used image analysis pipelines.

bioinformatics↗

bioRxiv2022

The funny current If is essential for the fight-or-flight response in cardiac pacemaker cells

The sympathetic nervous system fight-or-flight response is characterized by a rapid increase in heart rate, which is mediated by an increase in the spontaneous action potential (AP) firing rate of pacemaker cells in the sinoatrial node. Sympathetic neurons stimulate sinoatrial myocytes (SAMs) by activating {beta} adrenergic receptors ({beta}ARs) and increasing cAMP. The funny current (If) is among the cAMP-sensitive currents in SAMs. If is critical for pacemaker activity, however, its role in the fight-or-flight response remains controversial. In this study, we used AP waveform analysis, machine learning, and dynamic clamp experiments in acutely-isolated SAMs from mice to quantitatively define the AP waveform changes and role of If in the fight-or-flight response. We found that while {beta}AR stimulation significantly altered nearly all AP waveform parameters, the increase in AP firing rate was only correlated with changes in a subset of parameters (diastolic duration, late AP duration, and diastolic depolarization rate). Dynamic clamp injection of the {beta}AR-sensitive component of If showed that it accounts for approximately 41% of the fight-or-flight increase in AP firing rate and 60% of the decrease in the interval between APs. Thus, If is an essential contributor to the fight-or-flight increase in heart rate.

physiology↗