Rapid tissue prototyping with micro-organospheres.

In vitro tissue models hold great promise for modeling diseases and drug responses. Here, we used emulsion microfluidics to form micro-organospheres (MOSs), which are droplet-encapsulated miniature three-dimensional (3D) tissue models that can be established rapidly from patient tissues or cells. MOSs retain key biological features and responses to chemo-, targeted, and radiation therapies compared with organoids. The small size and large surface-to-volume ratio of MOSs enable various applications including quantitative assessment of nutrient dependence, pathogen-host interaction for anti-viral drug screening, and a rapid potency assay for chimeric antigen receptor (CAR)-T therapy. An automated MOS imaging pipeline combined with machine learning overcomes plating variation, distinguishes tumorspheres from stroma, differentiates cytostatic versus cytotoxic drug effects, and captures resistant clones and heterogeneity in drug response. This pipeline is capable of robust assessments of drug response at individual-tumorsphere resolution and provides a rapid and high-throughput therapeutic profiling platform for precision medicine.

Patient-derived micro-organospheres enable clinical precision oncology.

Patient-derived xenografts (PDXs) and patient-derived organoids (PDOs) have been shown to model clinical response to cancer therapy. However, it remains challenging to use these models to guide timely clinical decisions for cancer patients. Here, we used droplet emulsion microfluidics with temperature control and dead-volume minimization to rapidly generate thousands of micro-organospheres (MOSs) from low-volume patient tissues, which serve as an ideal patient-derived model for clinical precision oncology. A clinical study of recently diagnosed metastatic colorectal cancer (CRC) patients using an MOS-based precision oncology pipeline reliably assessed tumor drug response within 14 days, a timeline suitable for guiding treatment decisions in the clinic. Furthermore, MOSs capture original stromal cells and allow T cell penetration, providing a clinical assay for testing immuno-oncology (IO) therapies such as PD-1 blockade, bispecific antibodies, and T cell therapies on patient tumors.

Machine learning enables detection of early-stage colorectal cancer by whole-genome sequencing of plasma cell-free DNA.

BACKGROUND: Blood-based methods using cell-free DNA (cfDNA) are under development as an alternative to existing screening tests. However, early-stage detection of cancer using tumor-derived cfDNA has proven challenging because of the small proportion of cfDNA derived from tumor tissue in early-stage disease. A machine learning approach to discover signatures in cfDNA, potentially reflective of both tumor and non-tumor contributions, may represent a promising direction for the early detection of cancer. METHODS: Whole-genome sequencing was performed on cfDNA extracted from plasma samples (N = 546 colorectal cancer and 271 non-cancer controls). Reads aligning to protein-coding gene bodies were extracted, and read counts were normalized. cfDNA tumor fraction was estimated using IchorCNA. Machine learning models were trained using k-fold cross-validation and confounder-based cross-validations to assess generalization performance. RESULTS: In a colorectal cancer cohort heavily weighted towards early-stage cancer (80% stage I/II), we achieved a mean AUC of 0.92 (95% CI 0.91-0.93) with a mean sensitivity of 85% (95% CI 83-86%) at 85% specificity. Sensitivity generally increased with tumor stage and increasing tumor fraction. Stratification by age, sequencing batch, and institution demonstrated the impact of these confounders and provided a more accurate assessment of generalization performance. CONCLUSIONS: A machine learning approach using cfDNA achieved high sensitivity and specificity in a large, predominantly early-stage, colorectal cancer cohort. The possibility of systematic technical and institution-specific biases warrants similar confounder analyses in other studies. Prospective validation of this machine learning method and evaluation of a multi-analyte approach are underway.

Microfluidic system with integrated microinjector for automated Drosophila embryo injection.

Drosophila is one of the most important model organisms in biology. Knowledge derived from the recently sequenced 12 genomes of various Drosophila species can today be combined with the results of more than 100 years of research to systematically investigate Drosophila biology at the molecular level. In order to enable automated, high-throughput manipulation of Drosophila embryos, we have developed a microfluidic system based on a Pyrex-silicon-Pyrex sandwich structure with integrated, surface-micromachined silicon nitride injector for automated injection of reagents. Our system automatically retrieves embryos from an external reservoir, separates potentially clustered embryos through a sheath flow mechanisms, passively aligns an embryo with the integrated injector through geometric constraints, and pushes the embryo onto the injector through flow drag forces. Automated detection of an embryo at injection position through an external camera triggers injection of reagents and subsequent ejection of the embryo to an external reservoir. Our technology can support automated screens based on Drosophila embryos as well as creation of transgenic Drosophila lines. Apart from Drosophila embryos, the layout of our system can be easily modified to accommodate injection of oocytes, embryos, larvae, or adults of other species and fills an important technological gap with regard to automated manipulation of multicellular organisms.

Enhanced cellular uptake and long-term retention of chitosan-modified iron-oxide nanoparticles for MRI-based cell tracking.

Tracking cells after therapeutic transplantation is imperative for evaluation of implanted cell fate and function. In this study, ultrasmall superparamagnetic iron oxide nanoparticles (USPIO NPs) were surface functionalized with water-soluble chitosan, a cationic polysaccharide that mediates enhanced endocytic uptake, endosomal escape into the cytosol, and subsequent long-term retention of nanoparticles. NP surface and chitosan were independently fluorescently labeled. Our NPs enable NP trafficking studies and determination of fate beyond uptake by fluorescence microscopy as well as tracking of labeled cells as localized regions of hypointensity in T(2)*-weighted magnetic resonance imaging (MRI) images. Adult rat neural stem cells (NSCs) were labeled with NPs, and assessment of NSC proliferation rates and differentiation potential revealed no significant differences between labeled and unlabeled NSCs. Significantly enhanced uptake of chitosan NPs in comparison to native NPs was confirmed by transmission electron microscopy, nuclear magnetic resonance (NMR) spectroscopy and in vitro cellular MRI at 11.7 Tesla. While only negligible fractions of native NPs enter cells, chitosan NPs appear within membranous vesicles within 2 hours of exposure. Additionally, chitosan-functionalized NPs escaped from membrane-bound vesicles within days, circumventing NP endo-lysosomal trafficking and exocytosis and hence enabling long-term tracking of labeled cells. Finally, our labeling strategy does not contain any NSC-specific reagents. To demonstrate general applicability across a variety of primary and immortalized cell types, embryonic mouse NSCs, mouse embryonic stem cells, HEK 293 kidney cells, and HeLa cervical cancer cells were additionally exposed to chitosan-USPIO NPs and exhibited similarly efficient loading as verified by NMR relaxometry. Our efficient and versatile labeling technology can support cell tracking with close to single cell resolution by MRI in vitro, for example, in complex tissue models not optically accessible by confocal or multi-photon fluorescence microscopy, and potentially in vivo, for example, in animal models of human disease or injury.