Insights from Genetic Phenotyping

"...we [developed] paired imaging and sequencing method to construct large-scale, multi-modal genotype-phenotypes maps in tissue with pooled genetic perturbations. Using imaging, we identify genetic perturbations in individual cells while simultaneously measuring their gene expression and subcellular morphology. Using single-cell sequencing, we measure transcriptomic responses to the same genetic perturbations." "In addition to modification of signaling proteins, imaging is also uniquely capable of detecting morphological changes beyond simple assessment of protein expression levels. We thus explored deep-learning-based approaches to explore more complex morphological phenotypes in the perturbation-imaging data. We used our autoencoder model to generate a lower-dimensional embedding for each image and then performed unbiased clustering on the embeddings. We illustrated these embeddings and the clustering with UMAP, using the imaging data of the lysosomal protein CathB as an example (Figure 5G). We then identified perturbations that shifted the representation of cells between the different clusters. For example, knockout of the lysosomal cholesterol transport gene Npc1 caused significant enrichment of CathB morphologies in clusters F and G, which have many distinct anti-CathB punctae, and depletion from clusters A and E, which have very few anti-CathB punctae (Figures 5H and 5I)." "Massively parallel in vivo genetic screens provide a powerful approach to dissect regulators of cellular and tissue physiology in their native context 38. Here, through extensive technical development and integration of multiple single cell profiling methods, we establish Perturb-Multi, a general and scalable approach for multimodal, pooled genetic screens with Perturb-seq- and multiplexed-imaging-based phenotyping. This multimodal in vivo approach allows us to access and interrogate physiological processes that are difficult to study by other means. We conduct screens in hepatocytes in the mouse liver and demonstrate how the resulting data enables causal dissection of multifarious aspects of liver biology, from hepatocyte zonation to steatosis to the signaling of nutritional status. Our data also provide a rich resource for future studies of liver biology and development of computational methods. In particular, our multimodal imaging data should prove useful for developing new analytical and machine learning tools for dimensionality reduction, feature extraction, and cross-modal analysis. More generally, this approach will enable the creation of large-scale training datasets for machine learning and AI efforts to build models of “virtual” cells, driving both new discoveries and new forms of understanding 37." https://lnkd.in/eSE7HAvp

Recently, a study published in Nature Immunology caught my eye. In it, the authors undertook an extensive study that charts generic variations influencing the tumour microenvironment (TME). The TME plays a crucial role in tumour progression and response to treatment. Understanding the genetic underpinnings of the TME could help pave the way for novel therapeutic approaches and enhanced treatment targeting. One of the study's most interesting aspects is its use of machine learning methods and advanced bioinformatic approaches to analyze and integrate large-scale datasets. The advanced computational methods used enabled identification of genetic variations that may have otherwise been overlooked, highlighting the power of computational biology in advancing our understanding of cancer. Leveraging these techniques, the researchers created a detailed atlas of genetic factors impacting the TME, which they refer to as immunity quantitative trait loci (immunQTLs), and showed that many of these genetic factors were likely co-localized with previously known expression quantitative trait loci. This observation suggests that the immunQTLs may contribute to the cellular heterogeneity observed within the TME by influencing the expression of genes modulating immune infiltration. Going beyond their initial discovery-driven computational work to further validate their findings, they mapped immunQTLs across >1,600 genes and 23 cancers that are associated with cancer pathogenesis and immune regulation. Diving even deeper, they went on to experimentally validate that one of the identified genes, CCL2, which is implicated in promoting colorectal carcinoma (CRC) progression by allowing tumour cells to evade immunity, may be a promising therapeutic target. This finding demonstrates the potential of the depth of the data set and how it might be used to identify and validate targets. This publication presents a significant amount of work that I have only scratched the surface of here. It offers new insights into the complexity of genetic factors influencing the TME, providing a comprehensive genetic map of the TME and its implications for cancer therapy. The authors have made their data available through a publicly accessible database to help propel further work by the research community. To me, an exciting aspect of this work is that it may help open the door to future combination therapeutic approaches that target both the tumour cells and their microenvironment. https://lnkd.in/ezRckvFh

𝐃𝐍𝐀 𝐏𝐡𝐞𝐧𝐨𝐭𝐲𝐩𝐢𝐧𝐠 (𝐁𝐢𝐨𝐥𝐨𝐠𝐢𝐜𝐚𝐥 𝐰𝐢𝐭𝐧𝐞𝐬𝐬) DNA phenotyping is an advanced forensic technique that predicts an individual's physical appearance and ancestry from genetic material. Unlike traditional DNA analysis, which focuses on matching DNA samples to known profiles, 𝐩𝐡𝐞𝐧𝐨𝐭𝐲𝐩𝐢𝐧𝐠 𝐫𝐞𝐜𝐨𝐧𝐬𝐭𝐫𝐮𝐜𝐭𝐬 𝐮𝐧𝐤𝐧𝐨𝐰𝐧 𝐢𝐧𝐝𝐢𝐯𝐢𝐝𝐮𝐚𝐥𝐬' 𝐜𝐡𝐚𝐫𝐚𝐜𝐭𝐞𝐫𝐢𝐬𝐭𝐢𝐜𝐬—such as eye color, hair color, skin tone, facial structure, and even age progression—based on genetic markers. This innovation has significantly enhanced investigative capabilities in law enforcement, missing persons cases, and historical research. In the modern era, 𝐀𝐈-𝐝𝐫𝐢𝐯𝐞𝐧 𝐠𝐞𝐧𝐨𝐦𝐢𝐜 𝐚𝐧𝐚𝐥𝐲𝐬𝐢𝐬 and 𝐦𝐚𝐜𝐡𝐢𝐧𝐞 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐚𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬 have refined DNA phenotyping, making it more precise and reliable. Countries like the United States and the United Kingdom actively use this technology for criminal investigations, where conventional leads are unavailable. Ethical concerns, however, remain a challenge, as predictive genetic profiling raises privacy issues and the risk of racial profiling. To mitigate these concerns, stringent regulations and oversight are necessary to ensure 𝐃𝐍𝐀 𝐩𝐡𝐞𝐧𝐨𝐭𝐲𝐩𝐢𝐧𝐠 𝐢𝐬 𝐮𝐬𝐞𝐝 𝐫𝐞𝐬𝐩𝐨𝐧𝐬𝐢𝐛𝐥𝐲—balancing technological advancements with ethical considerations. As forensic science evolves, DNA phenotyping is poised to become a crucial tool in solving complex cases and identifying unknown individuals. Challenges in DNA Phenotyping 1. Privacy Concerns – Risk of unauthorized use or data breaches of genetic information. 2. Ethical and Legal Issues – Lack of clear legal frameworks and potential for genetic discrimination. 3. Risk of Racial Profiling – Possibility of reinforcing biases in law enforcement. 4. Accuracy Limitations – Predictions are not 100% accurate, leading to potential misidentifications. 5. High Costs – Requires advanced technology and skilled professionals, making it expensive. 6. Public Trust Issues – Skepticism in legal systems and among the general public regarding its reliability. #DNAPhenotyping #ForensicScience #GeneticAnalysis #CrimeInvestigation #FutureOfForensics #Biotechnology #LawEnforcement #ModernForensics #DNAProfiling #TechInForensics