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Editor-in-Chief Michelle Itano (University of North Carolina, Chapel Hill, NC, USA), shares her selection of the publications from the journal over the last 3 months. Read on for a new workflow for more reproducible RNAi screening, an alternative diagnostic approach for a gene that confers resistance to from Toxoplasma gondii-induced encephalitis and to get up to date with the latest advances in AI for image analysis.
A QC-guided framework for performance assessment in plate-based RNAi screening with fluorescence and metabolic readouts
RNA interference (RNAi) screening is an approach commonly used in functional genomics that enables sequence-specific gene silencing through the delivery of synthetic small interfering RNAs (siRNA). However, achieving reproducible readouts remains a challenge due to variability in assay design and performance. To address this issue, the authors developed a workflow designed to improve assay quality and reproducibility in RNAi screenings.
The workflow combines green fluorescent protein (GFP) fluorescence measurements, taken throughout a 72-hour period, with an endpoint XTT metabolic assay to assess cell activity. The authors also established a corresponding set of quality control (QC) steps to assess control separation, variability and replicate concordance, in order to assess assay performance. Using this QC framework, the authors established that 72 hours was the most reliable endpoint for downstream hit identification.
By identifying optimal assay time points and enabling continuous assessment of assay performance, the novel workflow and QC framework combined enable researchers to design RNAi screening experiments with more confidence, improving reproducibility and data quality.
A PCR method to detect the mouse major histocompatibility complex H2-Ld gene that confers resistance to Toxoplasma encephalitis
This study describes the development of a PCR-based method to detect the H2-Ld gene in ear biopsies from live mice. The H2-Ld gene confers natural resistance to encephalitis from Toxoplasma gondii infections. Traditionally, this gene has been detected using flow cytometry analyses of spleen tissue, a process that requires the mouse to be sacrificed or to undergo an invasive splenectomy, which has health and welfare implications. The method described by the researchers uses PCR to detect the H2-Ld gene, working with samples from ear biopsies, reducing cost and preparation time while preserving the lives of the mice. The approach described makes it easier to select mice with the desired genotypes for breeding or other in vivo procedures.
Recent advances in deep learning for biological microscopy image analysis beyond segmentation
The applications of AI across biological research continue to expand daily, transforming the field from experimental design and image analysis to biological interpretation. This review explores how deep learning is being integrated into microscopy, ranging from assay design to image processing and analysis. The authors explore the role that deep learning can play in supporting the automation of processes involving the identification of complex cellular patterns, reducing the need for manual selection of features. The review also discusses the future of closed-loop microscopy where deep learning can be used to analyse images and guide subsequent experimental steps in real time, enabling experiments to adapt based on the results obtained.
Importantly, the authors emphasise the need for AI models to undergo rigorous validation to ensure reliable, accurate and biologically meaningful results. The article provides insight into one of the many areas where AI is being adopted in biological research.
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