Biology subjects

Schmacke, N. A.

Publications and source records attributed to Schmacke, N. A..

7 recordsLinked to original sources

bioRxiv2026

Optimizing CRISPR/Cas9 genome editing in primary human hematopoietic cells to advance studies into HIV biology

Defining how human host factors shape HIV-1 infection in vivo remains essential for the development of genetically engineered cell therapies. Here, we established a non-viral CRISPR/Cas9 ribonucleoprotein-based platform for efficient single and multiplex gene editing in primary human CD34+ hematopoietic stem and progenitor cells (HSPCs). Edited HSPCs retained viability, proliferative capacity, primitive immunophenotypes, and multilineage differentiation potential and supported targeted reporter knock-in independently of the cell source, cord blood (CB), bone marrow (BM), and mobilized from peripheral blood (MPB). Following differentiation, SAMHD1 knockout increased HIV-1 susceptibility of HSPC-derived macrophages, MX2 knockout also increased HIV infection in cells pre-stimulated with IFN-2a, and CXCR4 knockout blocked X4-tropic HIV-1 infection of HSPC-derived megakaryocytes. We then transplanted CCR5-, SAMHD1-, or non-targeting control-edited HSPCs into immunodeficient mice and challenged reconstituted animals with R5-tropic HIV-1. CCR5 knockout prevented detectable viral spread, validating the model using a clinically relevant dependency factor. In contrast, SAMHD1 knockout accelerated viral dissemination, with earlier plasma viremia and 3.6-fold higher cumulative viremia, although endpoint viral burden was not significantly different from controls. These findings establish transplantation of CRISPR/Cas9-edited human HSPCs as a modular platform for dissecting HIV-1 host-factor function across hematopoietic lineages ex vivo and in vivo and for evaluating engineered cell-based strategies.

bioengineering↗

bioRxiv2025

A vesicle-based platform for high-efficiency, high-viability CRISPR/Cas9 knockout in primary human myeloid cells

Myeloid cells, including monocytes, macrophages, and dendritic cells, are central to host defence, inflammation, and antigen presentation. However, functional genetics studies in corresponding primary cells remain limited due to inefficient delivery methods. Here we report a vesicle-based CRISPR/Cas9 platform for effective genome editing in primary human myeloid cells using engineered virus-like particles (VLPs) and extracellular vesicles (EVs) to deliver Cas9-gRNA ribonucleoproteins (RNPs). Targeting primary CD14+ monocytes enables rapid generation of robust polyclonal knockouts while preserving cell viability and subsequent differentiation into macrophages and dendritic cells. Because our workflow relies on standard cell-culture handling rather than electroporation instrumentation, it is inexpensive, highly scalable and readily transferable across laboratories. Edited cells remain compatible with diverse downstream assays and functional readouts, enabling scalable loss-of-function studies and mechanistic dissection of primary human myeloid cell biology in health and disease. Vesicle-mediated RNP delivery thus provides a broadly applicable route to genetic perturbation in primary human myeloid lineages. SummaryFiori and colleagues describe a vesicle-based platform for efficient CRISPR/Cas9 knockout in primary human myeloid cells. This approach preserves cell viability, enables near-complete gene disruption, and supports functional studies of innate immunity, antigen presentation, and HIV-1 infection.

immunology↗

bioRxiv2025

scPortrait integrates single-cell images into multimodal modeling

Machine learning increasingly uncovers rules of biology directly from data, enabled by large, standardized datasets. Microscopy images provide rich information on cellular architecture and are accessible at scale across biological systems, making them an ideal foundation for modeling cell behavior. However, a standardized image format does not exist at the single-cell level. Here we present scPortrait, an scverse software package for generation, storage, and application of single-cell image datasets. scPortrait reads, stitches and segments raw fields of view with out-of-core computation scaling to larger-than-memory datasets. Parallelization enables rapid extraction of individual cells into a standardized single-cell image format with fast access to accelerate machine learning. scPortrait enables analysis across modalities including images, proteomics and transcriptomics, identifying cancer-associated macrophage subpopulations by morphology and embedding single-cell images into transcriptome atlases. scPortrait turns microscopy images into a reusable resource for integrative cell modeling, establishing single-cell images as a core modality in systems biology.

systems biology↗

bioRxiv2024

The proteomic landscape of proteotoxic stress in a fibrogenic liver disease

Protein misfolding diseases, including alpha-1 antitrypsin deficiency (AATD), pose significant health challenges, with their cellular progression still poorly understood1-3. We utilize spatial proteomics by mass spectrometry and machine learning to map AATD in human liver tissue. Combining Deep Visual Proteomics (DVP) with single-cell analysis4,5, we probe intact patient biopsies to resolve molecular events during hepatocyte stress in pseudo-time across fibrosis stages. We achieve unprecedented proteome depth of up to 3,800 proteins from a third of a single cell in formalin-fixed, paraffin-embedded (FFPE) tissue. This dataset revealed a potentially clinically actionable peroxisomal upregulation that precedes the canonical unfolded protein response. Our single-cell proteomics data show alpha-1 antitrypsin accumulation is largely cell-intrinsic, with minimal stress propagation between hepatocytes. We integrated proteomic data with AI-guided image-based phenotyping across multiple disease stages, revealing a terminal hepatocyte state characterized by globular protein aggregates and distinct proteomic signatures, notably including elevated TNFSF10/TRAIL expression. This phenotype may represent a critical disease progression stage. Our study offers novel insights into AATD pathogenesis and introduces a powerful methodology for high-resolution, in situ proteomic analysis of complex tissues. This approach holds potential to unravel molecular mechanisms in various protein misfolding disorders, setting a new standard for understanding disease progression at the single-cell level in human tissue.

cell biology↗

bioRxiv2024

Scalable and universal prediction of cellular phenotypes

Biological systems can be interrogated by perturbing individual components and observing the consequences across molecular, cellular, and phenotypic levels. The vast combinatorial space of possible perturbations and responses makes exhaustive experimentation infeasible. Recent advances in machine learning have shown that training on diverse datasets enables transfer learning across tasks, capturing patterns that generalize and improving performance on previously unseen problems. Inspired by this principle, we present Prophet, a transformer-based model pretrained on a vast, heterogeneous collection of perturbation experiments. This pretraining allows Prophet to predict the outcomes of untested genetic or chemical perturbations in novel cellular contexts, spanning phenotypes such as gene expression, viability, and morphology. By leveraging shared structure across apparently disconnected assays, Prophet provides a scalable framework for large-scale virtual screening and prioritization of informative experiments. Prophet consistently outperforms baseline models, including those trained on single phenotypes, showing that transfer learning between phenotypes not only is possible but improves predictive accuracy. Its capabilities extends to in vivo developmental systems, where it recapitulates known lineage biology and proposes new candidates. In a large-scale in silico screen for melanoma, Prophet identified and experimentally validated compounds with selective activity that mirrored clinically approved therapies, demonstrating its ability to transform perturbation biology into a predictive and scalable engine for therapeutic discovery.

cell biology↗

bioRxiv2023

SPARCS, a platform for genome-scale CRISPR screening for spatial cellular phenotypes

Forward genetic screening associates phenotypes with genotypes by randomly inducing mutations and then identifying those that result in phenotypic changes of interest. Here we present spatially resolved CRISPR screening (SPARCS), a platform for microscopy-based genetic screening for spatial cellular phenotypes. SPARCS uses automated high-speed laser microdissection to physically isolate phenotypic variants in situ from virtually unlimited library sizes. We demonstrate the potential of SPARCS in a genome-wide CRISPR-KO screen on autophagosome formation in 40 million cells. Coupled to deep learning image analysis, SPARCS recovered almost all known macroautophagy genes in a single experiment and discovered a role for the ER-resident protein EI24 in autophagosome biogenesis. Harnessing the full power of advanced imaging technologies, SPARCS enables genome-wide forward genetic screening for diverse spatial phenotypes in situ.

systems biology↗

bioRxiv2019

Priming enables a NEK7-independent route of NLRP3 activation

The NLRP3 inflammasome plays a central role in antimicrobial defense, as well as in sterile inflammatory conditions. NLRP3 activity is governed by two independent signals. The first signal primes NLRP3, allowing it to respond to its activation signal. In the murine system, the mitotic spindle kinase NEK7 has been identified as a crucial factor in relaying the activation signal to NLRP3. Here we show that the requirement for NEK7 can be bypassed by TAK1-dependent post-translational priming. Under pro-inflammatory conditions that activate TAK1, NEK7 was dispensable for NLRP3 inflammasome formation in human and murine cells. Intriguingly, dissecting the NEK7 requirement in iPSC-derived primary human macrophages revealed that this NEK7-independent mechanism constitutes the predominant NLRP3 priming pathway in these cells. In summary, our results suggest that NEK7 functions as an NLRP3 priming - rather than activation - factor that can work in synergy or redundancy with other priming pathways to accelerate inflammasome activation.

immunology↗