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Droplet microfluidic systems have been widely deployed to interrogate biological and chemical systems. The major limitations of these systems are the relatively high error rates from critical droplet manipulation functions. To address these limitations, we describe the development of FIDELITY (Flotation and Interdigitated electrode forces on Droplets to Enable Lasting system IntegriTY), a highly sensitive and accurate size-based droplet bandpass filter that leverages the natural buoyancy of aqueous droplets and highly localized dielectrophoretic force generated by interdigitated electrode arrays. Droplet manipulation accuracies greater than 99% were achieved at a throughput of up to 100 droplets/s and separation of droplets that differed in diameter by only 6 μm was demonstrated. Last, the utility of FIDELITY was demonstrated in a droplet size quality control application and also in a droplet-based in vitro transcription/translation workflow. We anticipate FIDELITY to be integrated into a broad range of droplet microfluidic configurations to achieve exceptional operational accuracy.

Separation and collection of blood plasma are required steps for broad ranges of clinical diagnostic assays. Here, we introduce a continuous-flow blood plasma separation microfluidic device based on size exclusion cell separation in crossflow microchannels. A gradient-width structure is utilized to minimize blood cell leakage into the plasma collection channel while maximizing the flow rate for higher throughput. Additionally, the crossflow channels are placed at the corner of the blood channel to enhance Fahraeus effects to reduce the concentration of blood cells around the entrance of the crossflow channels. Combined with a circular geometry that minimizes the footprint of the device for point-of-care diagnosis applications, the developed device significantly improves the performance of blood plasma separation in terms of separation efficiency, degree of hemolysis, and microchannel clogging by blood cells. At a relatively high flow rate of 2,000 µl/h for a passive blood separation device, a plasma extraction ratio of 22% and red blood cell separation efficiency of 97% could be achieved using undiluted whole human blood, which is about 28% higher compared with the conventional crossflow design. The effect of channel height and flow rate on the degree of hemolysis was also investigated, where the optimized device could achieve very little hemolysis (0.01% to 0.5%), demonstrating that the device can produce high-quality blood plasma. Integration of this high-performance, compact, and passive blood plasma separation microdevice with on-chip blood biomarker sensing elements can create a point-of-care diagnosis system with reduced cost, test time, and sample/reagent volume.

Conversion of adenosine to inosine is a frequent type of RNA editing, but important details about the biology of this conversion remain unknown because of a lack of imaging tools. We developed inoFISH to directly visualize and quantify adenosine-to-inosine-edited transcripts in situ. We found that editing of the GRIA2, EIF2AK2, and NUP43 transcripts is uncorrelated with nuclear localization and paraspeckle association. Further, NUP43 exhibits constant editing levels between single cells, while GRIA2 editing levels vary.

Non-genetic factors can cause individual cells to fluctuate substantially in gene expression levels over time. It remains unclear whether these fluctuations can persist for much longer than the time of one cell division. Current methods for measuring gene expression in single cells mostly rely on single time point measurements, making the duration of gene expression fluctuations or cellular memory difficult to measure. Here, we combined Luria and Delbrück’s fluctuation analysis with population-based RNA sequencing (MemorySeq) for identifying genes transcriptome-wide whose fluctuations persist for several divisions. MemorySeq revealed multiple gene modules that expressed together in rare cells within otherwise homogeneous clonal populations. These rare cell subpopulations were associated with biologically distinct behaviors like proliferation in the face of anti-cancer therapeutics. The identification of non-genetic, multigenerational fluctuations can reveal new forms of biological memory in single cells, and suggests that non-genetic heritability of cellular state may be a quantitative property.

Non-genetic factors can cause individual cells to fluctuate substantially in gene expression levels over time. Yet it remains unclear whether these fluctuations can persist for much longer than the time of one cell division. Current methods for measuring gene expression in single cells mostly rely on single time point measurements, making the duration of gene expression fluctuations or cellular memory difficult to measure. Here, we report a method combining Luria and Delbrück’s fluctuation analysis with population-based RNA sequencing (MemorySeq) for identifying genes transcriptome-wide whose fluctuations persist for several cell divisions. MemorySeq revealed multiple gene modules that are expressed together in rare cells within otherwise homogeneous clonal populations. Further, we found that these rare cell subpopulations are associated with biologically distinct behaviors, such as the ability to proliferate in the face of anti-cancer therapeutics, in different cancer cell lines. The identification of non-genetic, multigenerational fluctuations has the potential to reveal new forms of biological memory at the level of single cells and suggests that non-genetic heritability of cellular state may be a quantitative property.

Although single-cell RNA sequencing can reliably detect large-scale transcriptional programs, it is unclear whether it accurately captures the behavior of individual genes, especially those that express only in rare cells. Here, we use single-molecule RNA fluorescence in situ hybridization as a gold standard to assess trade-offs in single-cell RNA-sequencing data for detecting rare cell expression variability. We quantified the gene expression distribution for 26 genes that range from ubiquitous to rarely expressed and found that the correspondence between estimates across platforms improved with both transcriptome coverage and increased number of cells analyzed. Further, by characterizing the trade-off between transcriptome coverage and number of cells analyzed, we show that when the number of genes required to answer a given biological question is small, then greater transcriptome coverage is more important than analyzing large numbers of cells. More generally, our report provides guidelines for selecting quality thresholds for single-cell RNA-sequencing experiments aimed at rare cell analyses.

The development of single cell RNA sequencing technologies has emerged as a powerful means of profiling the transcriptional behavior of single cells, leveraging the breadth of sequencing measurements to make inferences about cell type. However, there is still little understanding of how well these methods perform at measuring single cell variability for small sets of genes and what “transcriptome coverage” (e.g. genes detected per cell) is needed for accurate measurements. Here, we use single molecule RNA FISH measurements of 26 genes in thousands of melanoma cells to provide an independent reference dataset to assess the performance of the DropSeq and Fluidigm single cell RNA sequencing platforms. We quantified the Gini coefficient, a measure of rare-cell expression variability, and find that the correspondence between RNA FISH and single cell RNA sequencing for Gini, unlike for mean, increases markedly with per-cell library complexity up to a threshold of ∼2000 genes detected. A similar complexity threshold also allows for robust assignment of multi-genic cell states such as cell cycle phase. Our results provide guidelines for selecting sequencing depth and complexity thresholds for single cell RNA sequencing. More generally, our results suggest that if the number of genes whose expression levels are required to answer any given biological question is small, then greater transcriptome complexity per cell is likely more important than obtaining very large numbers of cells.

The purpose of this study was to elucidate the role of nucleotide binding oligomerization domain-containing protein 2 (NOD2) signaling in atherosclerosis and periodontal bone loss using an Apolipoprotein E(-/-) (ApoE(-/-)) mouse model based on the proposed role of NOD2 in inflammation. NOD2(-/-)ApoE(-/-) and ApoE(-/-) mice fed a standard chow diet were given an oral gavage of Porphyromonas gingivalis for 15 wk. NOD2(-/-)ApoE(-/-) mice exhibited significant increases in inflammatory cytokines, alveolar bone loss, cholesterol, and atherosclerotic lesions in the aorta and the heart compared with ApoE(-/-) mice. In contrast, ApoE(-/-) mice injected i.p. with Muramyl DiPeptide (MDP) to stimulate NOD2 and given an oral gavage of P. gingivalis displayed a reduction of serum inflammatory cytokines, alveolar bone loss, cholesterol, and atherosclerotic lesions in the aorta and aortic sinus compared with ApoE(-/-) mice orally challenged but injected with saline. A reduction in body weight gain was observed in ApoE(-/-) mice fed a high-fat diet (HFD) and injected with MDP compared with ApoE(-/-) mice fed a high-fat diet but injected with saline. MDP treatment of bone marrow-derived macrophages incubated with P. gingivalis increased mRNA expressions of NOD2, Toll-like receptor 2, myeloid differentiation primary response gene 88, and receptor-interacting protein-2 but reduced the expressions of inhibitor of NF-κB kinase-β, NF-κB, c-Jun N-terminal kinase 3, and TNF-α protein levels compared with saline control, highlighting pathways involved in MDP antiinflammatory effects. MDP activation of NOD2 should be considered in the treatment of inflammatory processes affecting atherosclerosis, periodontal bone loss ,and possibly, diet-induced weight gain.

Aim The purpose of this study was to assess the role of anti-bone resorptive agents and an anti-inflammatory compound in murine Porphyromonas gingivalis (P. gingivalis)-induced periodontitis. Material and Methods Six randomly assigned groups were administered vehicle (saline, control) (n = 6), P. gingivalis infection only (untreated) (n = 6), human-Fc (n = 4), Kavain (n = 6), OPG-Fc (n = 6) and Receptor activator of nuclear factor-kappa B (RANK)-Fc (n = 6) intraperitoneally at day 0, 3 and 7. Animals were euthanized on day 10 and subjected to comprehensive histomorphometric analysis. To capture the progress of inflammation, serum samples were collected at days 0, 3, 7 and 10 for levels of pro-inflammatory cytokines. Results Compared with control group, OPG-Fc, RANK-Fc and Kavain treatment showed significant bone loss reduction with OPG-Fc performing better than RANK-Fc or Kavain. Epithelial down-growth showed significant reduction in treatment groups with OPG-Fc performing better than RANK-Fc or Kavain. Finally, Kavain, OPG-Fc and RANK-Fc-treated mice displayed reduced inflammatory cell counts and cytokine expression particularly at day 7 postinfection. Conclusions RANKL antagonists and Kavain effectively reduced alveolar bone loss in P. gingivalis-induced periodontitis in our mice model. Compared with RANK-Fc, Kavain-treated animals showed milder improvement of bone and connective tissue inflammation. Therapeutic implications in the prevention of periodontal bone loss are discussed.