analysis pipeline

Overcoming Treatment Resistance by Targeting Polyploid Breast Cancer Cells with AI assisted Single-Cell Analysis

Therapy resistance remains a formidable challenge in breast cancer treatment, with emerging evidence identifying polyploid giant cancer cells (PGCCs) as key drivers. These cells, arising through whole-genome doubling (WGD) events, exhibit enhanced resistance to therapies, contributing to disease relapse. PGCCs are characterized by enlarged cell and nuclear sizes, increased DNA content, and greater resilience compared to non-PGCCs. Their prevalence escalates with disease progression and therapeutic stress, underscoring their critical role in treatment resistance. As such, we hypothesize that inhibiting polyploid cancer cells can effectively reduce therapeutic resistance. Despite this, effective strategies targeting PGCCs are limited, hindered by the lack of high-throughput methods to assess PGCC viability and abundance. Traditional screening assays lack the sensitivity to detect the elimination of small populations of PGCCs, while current detection methods, such as visual inspection and flow cytometry, are not suited for high-throughput compound screening. Our preliminary work has established a high-throughput single-cell morphological analysis pipeline capable of quantifying PGCCs, and we successfully screened 2,726 compounds for their efficacy on PGCCs. Based on the preliminary success, we aim to further improve its robustness and accuracy under diverse staining and imaging conditions, ensuring consistent performance across multiple labs for widespread use in PGCC/WGD studies, with deep learning to accelerate the discovery of therapeutic strategies targeting PGCCs. In addition to empirical screening, our scRNA-Seq analysis of PGCCs has revealed altered gene expression, particularly in genes associated with FOXM1, a transcription factor critical in cell cycle regulation and linked to poor outcomes in various cancers. PGCCs also show altered ferroptosis regulators and elevated reactive oxygen species (ROS), indicating susceptibility to ferroptosis. Here, we propose two independent and complementary aims. Aim 1: We will develop and validate a robust deep learning–based single-cell morphological analysis pipeline for accurate PGCC/non-PGCC discrimination across variable staining, imaging, and lab settings. The model will be benchmarked on independent datasets from external labs and released as open-source, version-controlled software with full documentation to support reproducibility and broad adoption in PGCC/WGD research. Aim 2: Leveraging our screen of 2,726 FDA-approved compounds and mechanistic studies of FOXM1 and ferroptosis, we will prioritize and validate therapies that eradicate PGCCs and reduce treatment resistance. Using patient- derived cells, 3D spheroids, and syngeneic/xenograft models, we will rigorously assess top candidates as monotherapy and in combination with standard-of-care agents. Successful completion of this project will accelerate PGCC/WGD research, advance therapeutic strategies to overcome breast cancer resistance, and especially deliver benefits to patients with high PGCC burden. Given the prevalence of WGD across solid tumors and its induction by standard therapies, our approach holds broad clinical relevance and translational impact.

Current and future trends in neuroimaging

With the advent of several different fMRI analysis tools and packages outside of the established ones (i.e., SPM, AFNI, and FSL), today's researcher may wonder what the best practices are for fMRI analysis. This talk will discuss some of the recent trends in neuroimaging, including design optimization and power analysis, standardized analysis pipelines such as fMRIPrep, and an overview of current recommendations for how to present neuroimaging results. Along the way we will discuss the balance between Type I and Type II errors with different correction mechanisms (e.g., Threshold-Free Cluster Enhancement and Equitable Thresholding and Clustering), as well as considerations for working with large open-access databases.

State-of-the-Art Spike Sorting with SpikeInterface

This webinar will focus on spike sorting analysis with SpikeInterface, an open-source framework for the analysis of extracellular electrophysiology data. After a brief introduction of the project (~30 mins) highlighting the basics of the SpikeInterface software and advanced features (e.g., data compression, quality metrics, drift correction, cloud visualization), we will have an extensive hands-on tutorial (~90 mins) showing how to use SpikeInterface in a real-world scenario. After attending the webinar, you will: (1) have a global overview of the different steps involved in a processing pipeline; (2) know how to write a complete analysis pipeline with SpikeInterface.

Pynapple: a light-weight python package for neural data analysis - webinar + tutorial

In systems neuroscience, datasets are multimodal and include data-streams of various origins: multichannel electrophysiology, 1- or 2-p calcium imaging, behavior, etc. Often, the exact nature of data streams are unique to each lab, if not each project. Analyzing these datasets in an efficient and open way is crucial for collaboration and reproducibility. In this combined webinar and tutorial, Adrien Peyrache and Guillaume Viejo will present Pynapple, a Python-based data analysis pipeline for systems neuroscience. Designed for flexibility and versatility, Pynapple allows users to perform cross-modal neural data analysis via a common programming approach which facilitates easy sharing of both analysis code and data.

Pynapple: a light-weight python package for neural data analysis - webinar + tutorial

In systems neuroscience, datasets are multimodal and include data-streams of various origins: multichannel electrophysiology, 1- or 2-p calcium imaging, behavior, etc. Often, the exact nature of data streams are unique to each lab, if not each project. Analyzing these datasets in an efficient and open way is crucial for collaboration and reproducibility. In this combined webinar and tutorial, Adrien Peyrache and Guillaume Viejo will present Pynapple, a Python-based data analysis pipeline for systems neuroscience. Designed for flexibility and versatility, Pynapple allows users to perform cross-modal neural data analysis via a common programming approach which facilitates easy sharing of both analysis code and data.

An adaptive analysis pipeline for automated denoising and evaluation of high-density electrophysiological recordings

COSYNE 2022

Independent Component Analysis for detecting nonlinearities in CO2-elicited BOLD fluctuations: an analysis pipeline for the study of the central control of breathing with fMRI