Data-analysis strategies for image-based cell profiling

Image-based cell profiling is a high-throughput strategy for the quantification of phenotypic differences among a variety of cell populations. It paves the way to studying biological systems on a large scale by using chemical and genetic perturbations. The general workflow for this technology involves image acquisition with high-throughput microscopy systems and subsequent image processing and analysis. Here, we introduce the steps required to create high-quality image-based (i.e., morphological) profiles from a collection of microscopy images. We recommend techniques that have proven useful in each stage of the data analysis process, on the basis of the experience of 20 laboratories worldwide that are refining their image-based cell-profiling methodologies in pursuit of biological discovery. The recommended techniques cover alternatives that may suit various biological goals, experimental designs, and laboratories' preferences.Peer reviewe

CP2Image: Generating High-Quality Single-Cell Images Using CellProfiler Representations

Single-cell high-throughput microscopy images contain key biological information underlying normal and pathological cellular processes. Image-based analysis and profiling are powerful and promising for extracting this information but are made difficult due to substantial complexity and heterogeneity in cellular phenotype. Hand-crafted methods and machine learning models are popular ways to extract cell image information. Representations extracted via machine learning models, which often exhibit good reconstruction performance, lack biological interpretability. Hand-crafted representations, on the contrary, have clear biological meanings and thus are interpretable. Whether these hand-crafted representations can also generate realistic images is not clear. In this paper, we propose a CellProfiler to image (CP2Image) model that can directly generate realistic cell images from CellProfiler representations. We also demonstrate most biological information encoded in the CellProfiler representations is well-preserved in the generating process. This is the first time hand-crafted representations be shown to have generative ability and provide researchers with an intuitive way for their further analysis

Data-analysis strategies for image-based cell profiling

Image-based cell profiling is a high-throughput strategy for the quantification of phenotypic differences among a variety of cell populations. It paves the way to studying biological systems on a large scale by using chemical and genetic perturbations. The general workflow for this technology involves image acquisition with high-throughput microscopy systems and subsequent image processing and analysis. Here, we introduce the steps required to create high-quality image-based (i.e., morphological) profiles from a collection of microscopy images. We recommend techniques that have proven useful in each stage of the data analysis process, on the basis of the experience of 20 laboratories worldwide that are refining their image-based cell-profiling methodologies in pursuit of biological discovery. The recommended techniques cover alternatives that may suit various biological goals, experimental designs, and laboratories' preferences

Scalable graph based single cell omics analysis

The last few years have seen tremendous growth in the generation of large scale, high dimensional complex single cell datasets that map cellular heterogeneity and development across entire organisms. The analysis of these ‘cellular atlases’ in order to harness useful biological insights into the development of healthy tissues and organs, as well as pathogenesis, places new requirements on the capabilities of single-cell analysis computational tools. This covering document summarizes the key contributions of the author towards two single-cell analysis tasks that are common to many single-cell pipelines, namely clustering [PARC Stassen et al., 2020] and trajectory inference [VIA Stassen et al., 2021]. The complexity and stochastic nature of single-cell data presents various challenges to its analysis. Certain distortions and computational bottlenecks only manifest themselves at high cell counts or high dimensionality. Many recent efforts are aimed at efficiently distilling information without resorting to techniques that oversimplify the data in terms of cell count (e.g. subsampling which may remove rarer cells and reduce heterogeneity) or excessive dimensionality reduction (relying on just a few dimensions from an embedding that loses global neighborhood relationships). The two new methods introduced in the text both utilize graph-based approaches to modeling single cell data, with an emphasis on maintaining accuracy in terms of detecting cell types, preserving inter-cellular relationships, and offering a fast and data driven approach to parameter selection even as the scale of cells exceeds 100,000s and even million of cells. The performance of PARC and VIA have been validated on a wide range of datasets and the methods have been well received by the single-cell community as seen by the download statistics and integration into various pipelines