CellProfiler has enabled the scientific research community to create flexible, modular image analysis pipelines since its release in 2005. Here, we describe CellProfiler 3.0, a new version of the software supporting both whole-volume and plane-wise analysis of three-dimensional (3D) image stacks, increasingly common in biomedical research. CellProfiler’s infrastructure is greatly improved, and we provide a protocol for cloud-based, large-scale image processing. New plugins enable running pretrained deep learning models on images. Designed by and for biologists, CellProfiler equips researchers with powerful computational tools via a well-documented user interface, empowering biologists in all fields to create quantitative, reproducible image analysis workflows. The “big-data revolution” has struck biology: it is now common for robots to prepare cell samples and take thousands of microscopy images. Looking at the resulting images by eye would be extremely tedious, not to mention subjective. Thus, many biologists find they need software to analyze images easily and accurately. The third major release of our free open-source software CellProfiler is designed to help biologists working with images, whether a few or thousands. Researchers can download an online example workflow (that is, a “pipeline”) or create their own from scratch. Pipelines are easy to save, reuse, and share, helping improve scientific reproducibility. In this release, we’ve added the capability to find and measure objects in three-dimensional (3D) images. We’ve also made changes to CellProfiler’s underlying code to make it faster to run and easier to install, and we’ve added the ability to process images in the cloud and using neural networks (deep learning). We’ve also added more explanations to CellProfiler’s settings to help new users get started. We hope these changes will make CellProfiler an even better tool for current users and will provide new users better ways to get started doing quantitative image analysis.

We developed a novel asymmetric depth filtration (DF) approach to isolate extracellular vesicles (EVs) from biological fluids that outperforms ultracentrifugation and size‐exclusion chromatography in purity and yield of isolated EVs. By these metrics, a single‐step DF matches or exceeds the performance of multistep protocols with dedicated purification procedures in the isolation of plasma EVs. We demonstrate the selective transit and capture of biological nanoparticles in asymmetric pores by size and elasticity, low surface binding to the filtration medium, and the ability to cleanse EVs held by the filter before their recovery with the reversed flow all contribute to the achieved purity and yield of preparations. We further demonstrate the method's versatility by applying it to isolate EVs from different biofluids (plasma, urine, and cell culture growth medium). The DF workflow is simple, fast, and inexpensive. Only standard laboratory equipment is required for its implementation, making DF suitable for low‐resource and point‐of‐use locations. The method may be used for EV isolation from small biological samples in diagnostic and treatment guidance applications. It can also be scaled up to harvest therapeutic EVs from large volumes of cell culture medium.

The concept of liquid biopsy has emerged as a novel approach for cancer screening, which is based on the analysis of circulating cancer biomarkers in body fluids. Among the various circulating cancer biomarkers, including Food and Drug Administration (FDA)-approved circulating tumor cells (CTC) and circulating tumor DNA (ctDNA), exosomes have attracted tremendous attention due to their ability to diagnose cancer in its early stages with high efficiency. Recently, surface-enhanced Raman spectroscopy (SERS) has been applied for the detection of cancer exosomes due to its high sensitivity, specificity, and multiplexing capability. In this article, we review recent progress in the development of SERS-based technologies for in vitro identification of circulating cancer exosomes. The accent is made on the detection strategies and interpretation of the SERS data. The problems of detecting cancer-derived exosomes from patient samples and future perspectives of SERS-based diagnostics are also discussed.