A microfluidic platform for combinatorial experiments

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Biological Engineering, 2018.Page 165 blank. Cataloged from PDF version of thesis.Includes bibliographical references (pages 151-164).Experiments in biology are often combinatorial in nature and require analysis of large multi-dimensional spaces, but the scales of these experiments are limited by logistical complexity, cost, and reagent consumption. By miniaturizing experiments across nanoliter-scale emulsions that can be processed at large scales, droplet microfluidic platforms are poised to attack these challenges. Here we describe a droplet microfluidic platform for combinatorial experiments that automates the assembly of reagent combinations, with order-of-magnitude improvements over conventional liquid handling. Moreover, our design is accessible, requiring only standard lab equipment such as micropipettes, and improves the chemical compatibility of droplet microfluidic platforms for small molecules. We applied our platform to two experimental problems: combinatorial drug screening and microbial ecology. First, we used our platform to enable screening of pairwise combinations of a panel of antibiotics and 4,000+ investigational and approved drugs to overcome intrinsic antibiotic resistance in the model Gram-negative bacterial pathogen E. coli. This screen processed 4+ million droplet-level assays by hand in just 10 days to discover more than 10 combinations of antibiotics and non-antibiotic drugs for further study. We then applied our platform to microbial ecology, where the interactions between microbes in communities can dictate functions important for both basic science and biotechnology. As a proof of concept, we used our platform to survey 960 pairwise interactions of microbes isolated from soil, and deconstruct higher-order interactions in a 4-strain community. Altogether, we expect that our platform can be used to efficiently attack combinatorial problems across molecular and cellular biology.by Anthony Benjamin Kulesa.Ph. D

Sampling distributions and the bootstrap

The bootstrap can be used to assess uncertainty of sample estimates

Combinatorial drug discovery in nanoliter droplets

Combinatorial drug treatment strategies perturb biological networks synergistically to achieve therapeutic effects and represent major opportunities to develop advanced treatments across a variety of human disease areas. However, the discovery of new combinatorial treatments is challenged by the sheer scale of combinatorial chemical space. Here, we report a high-throughput system for nanoliter-scale phenotypic screening that formulates a chemical library in nanoliter droplet emulsions and automates the construction of chemical combinations en masse using parallel droplet processing. We applied this system to predict synergy between more than 4,000 investigational and approved drugs and a panel of 10 antibiotics against Escherichia coli, a model gram-negative pathogen. We found a range of drugs not previously indicated for infectious disease that synergize with antibiotics. Our validated hits include drugs that synergize with the antibiotics vancomycin, erythromycin, and novobiocin, which are used against gram-positive bacteria but are not effective by themselves to resolve gram-negative infections. Keywords: high-throughput screening; nanoliter droplet; drug synergy; antibiotics; small molecule

Optical High Content Nanoscopy of Epigenetic Marks Decodes Phenotypic Divergence in Stem Cells

While distinct stem cell phenotypes follow global changes in chromatin marks, single-cell chromatin technologies are unable to resolve or predict stem cell fates. We propose the first such use of optical high content nanoscopy of histone epigenetic marks (epi-marks) in stem cells to classify emergent cell states. By combining nanoscopy with epi-mark textural image informatics, we developed a novel approach, termed EDICTS (Epi-mark Descriptor Imaging of Cell Transitional States), to discern chromatin organizational changes, demarcate lineage gradations across a range of stem cell types and robustly track lineage restriction kinetics. We demonstrate the utility of EDICTS by predicting the lineage progression of stem cells cultured on biomaterial substrates with graded nanotopographies and mechanical stiffness, thus parsing the role of specific biophysical cues as sensitive epigenetic drivers. We also demonstrate the unique power of EDICTS to resolve cellular states based on epi-marks that cannot be detected via mass spectrometry based methods for quantifying the abundance of histone posttranslational modifications. Overall, EDICTS represents a powerful new methodology to predict single cell lineage decisions by integrating high content super-resolution nanoscopy and imaging informatics of the nuclear organization of epi-marks.National Institutes of Health (U.S.) (Grant GM110174

Massively parallel screening of synthetic microbial communities

Microbial communities have numerous potential applications in biotechnology, agriculture, and medicine. Nevertheless, the limited accuracy with which we can predict interspecies interactions and environmental dependencies hinders efforts to rationally engineer beneficial consortia. Empirical screening is a complementary approach wherein synthetic communities are combinatorially constructed and assayed in high throughput. However, assembling many combinations of microbes is logistically complex and difficult to achieve on a timescale commensurate with microbial growth. Here, we introduce the kChip, a droplets-based platform that performs rapid, massively parallel, bottom-up construction and screening of synthetic microbial communities. We first show that the kChip enables phenotypic characterization of microbes across environmental conditions. Next, in a screen of ∼100,000 multispecies communities comprising up to 19 soil isolates, we identified sets that promote the growth of the model plant symbiont Herbaspirillum frisingense in a manner robust to carbon source variation and the presence of additional species. Broadly, kChip screening can identify multispecies consortia possessing any optically assayable function, including facilitation of biocontrol agents, suppression of pathogens, degradation of recalcitrant substrates, and robustness of these functions to perturbation, with many applications across basic and applied microbial ecology.National Science Foundation (U.S.). Graduate Research Fellowship (Fellow ID 2016220942)National Science Foundation (U.S.). Graduate Research Fellowship (Fellow ID 2013164251)Burroughs Wellcome Fund (Career Award at the Scientific Interface Grant 1010240)Simons Foundation (Grant 542385

Multiplexed and high-throughput neuronal fluorescence imaging with diffusible probes

Synapses contain hundreds of distinct proteins whose heterogeneous expression levels are determinants of synaptic plasticity and signal transmission relevant to a range of diseases. Here, we use diffusible nucleic acid imaging probes to profile neuronal synapses using multiplexed confocal and super-resolution microscopy. Confocal imaging is performed using high-affinity locked nucleic acid imaging probes that stably yet reversibly bind to oligonucleotides conjugated to antibodies and peptides. Super-resolution PAINT imaging of the same targets is performed using low-affinity DNA imaging probes to resolve nanometer-scale synaptic protein organization across nine distinct protein targets. Our approach enables the quantitative analysis of thousands of synapses in neuronal culture to identify putative synaptic sub-types and co-localization patterns from one dozen proteins. Application to characterize synaptic reorganization following neuronal activity blockade reveals coordinated upregulation of the post-synaptic proteins PSD-95, SHANK3 and Homer-1b/c, as well as increased correlation between synaptic markers in the active and synaptic vesicle zones.National Institutes of Health (Award 1U01MH106011)National Institutes of Health (Award R01-MH112694)National Science Foundation (Grant 1305537)National Science Foundation (Grant 1707999)National Institute of Environmental Health Sciences (Grant P30-ES002109