Dimitra Pouli

A central challenge to the development of protein-based therapeutics is the inefficiency of delivery of protein cargo across the mammalian cell membrane, including escape from endosomes. Here we report that combining bioreducible lipid nanoparticles with negatively supercharged Cre recombinase or anionic Cas9:single-guide (sg)RNA complexes drives the electrostatic assembly of nanoparticles that mediate potent protein delivery and genome editing. These bioreducible lipids efficiently deliver protein cargo into cells, facilitate the escape of protein from endosomes in response to the reductive intracellular environment, and direct protein to its intracellular target sites. The delivery of supercharged Cre protein and Cas9:sgRNA complexed with bioreducible lipids into cultured human cells enables gene recombination and genome editing with efficiencies greater than 70%. In addition, we demonstrate that these lipids are effective for functional protein delivery into mouse brain for gene recombination in vivo. Therefore, the integration of this bioreducible lipid platform with protein engineering has the potential to advance the therapeutic relevance of protein-based genome editing.

Abstract Computational pathology 1,2 has witnessed considerable progress in the development of both task-specific predictive models and task-agnostic self-supervised vision encoders 3,4 . However, despite the explosive growth of generative artificial intelligence (AI), there have been few studies on building general-purpose multimodal AI assistants and copilots 5 tailored to pathology. Here we present PathChat, a vision-language generalist AI assistant for human pathology. We built PathChat by adapting a foundational vision encoder for pathology, combining it with a pretrained large language model and fine-tuning the whole system on over 456,000 diverse visual-language instructions consisting of 999,202 question and answer turns. We compare PathChat with several multimodal vision-language AI assistants and GPT-4V, which powers the commercially available multimodal general-purpose AI assistant ChatGPT-4 (ref. 6 ). PathChat achieved state-of-the-art performance on multiple-choice diagnostic questions from cases with diverse tissue origins and disease models. Furthermore, using open-ended questions and human expert evaluation, we found that overall PathChat produced more accurate and pathologist-preferable responses to diverse queries related to pathology. As an interactive vision-language AI copilot that can flexibly handle both visual and natural language inputs, PathChat may potentially find impactful applications in pathology education, research and human-in-the-loop clinical decision-making.

Monitoring subcellular functional and structural changes associated with metabolism is essential for understanding healthy tissue development and the progression of numerous diseases, including cancer, diabetes, and cardiovascular and neurodegenerative disorders. Unfortunately, established methods for this purpose either are destructive or require the use of exogenous agents. Recent work has highlighted the potential of endogenous two-photon excited fluorescence (TPEF) as a method to monitor subtle metabolic changes; however, mechanistic understanding of the connections between the detected optical signal and the underlying metabolic pathways has been lacking. We present a quantitative approach to detecting both functional and structural metabolic biomarkers noninvasively, relying on endogenous TPEF from two coenzymes, NADH (reduced form of nicotinamide adenine dinucleotide) and FAD (flavin adenine dinucleotide). We perform multiparametric analysis of three optical biomarkers within intact, living cells and three-dimensional tissues: cellular redox state, NADH fluorescence lifetime, and mitochondrial clustering. We monitor the biomarkers in cells and tissues subjected to metabolic perturbations that trigger changes in distinct metabolic processes, including glycolysis and glutaminolysis, extrinsic and intrinsic mitochondrial uncoupling, and fatty acid oxidation and synthesis. We demonstrate that these optical biomarkers provide complementary insights into the underlying biological mechanisms. Thus, when used in combination, these biomarkers can serve as a valuable tool for sensitive, label-free identification of changes in specific metabolic pathways and characterization of the heterogeneity of the elicited responses with single-cell resolution.

Alterations in the balance between different metabolic pathways used to meet cellular bioenergetic and biosynthetic demands are considered hallmarks of cancer. Optical imaging relying on endogenous fluorescence has been used as a noninvasive approach to assess tissue metabolic changes during cancer development. However, quantitative correlations of optical assessments with variations in the concentration of relevant metabolites or in the specific metabolic pathways that are involved have been lacking. In this study, we use high-resolution, depth-resolved imaging, relying entirely on endogenous two-photon excited fluorescence in combination with invasive biochemical assays and mass spectrometry to demonstrate the sensitivity and quantitative nature of optical redox ratio tissue assessments. We identify significant differences in the optical redox ratio of live, engineered normal and precancerous squamous epithelial tissues. We establish that while decreases in the optical redox ratio are associated with enhanced levels of glycolysis relative to oxidative phosphorylation, increases in glutamine consumption to support energy production are associated with increased optical redox ratio values. Such mechanistic insights in the origins of optical metabolic assessments are critical for exploiting fully the potential of such noninvasive approaches to monitor and understand important metabolic changes that occur in live tissues at the onset of cancer or in response to treatment.

The extracellular matrix (ECM) constituting up to 20% of the organ volume is a significant component of the brain due to its instructive role in the compartmentalization of functional microdomains in every brain structure. The composition, quantity, and structure of ECM changes dramatically during the development of an organism greatly contributing to the remarkably sophisticated architecture and function of the brain. Because fetal brain is highly plastic, we hypothesize that the fetal brain ECM may contain cues promoting neural growth and differentiation, highly desired in regenerative medicine. Thus, we studied the effect of brain-derived fetal and adult ECM complemented with matricellular proteins on cortical neurons using in vitro 3D bioengineered model of cortical brain tissue. The tested parameters included neuronal network density, cell viability, calcium signaling, and electrophysiology. Both adult and fetal brain ECM as well as matricellular proteins significantly improved neural network formation as compared to single-component, collagen I matrix. Additionally, the brain ECM improved cell viability and lowered glutamate release. The fetal brain ECM induced superior neural network formation, calcium signaling, and spontaneous spiking activity over adult brain ECM. This study highlights the difference in the neuroinductive properties of fetal and adult brain ECM and suggests that delineating the basis for this divergence may have implications for regenerative medicine.