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Cell lineage tracking is a long-standing and unresolved problem in biology. Microfluidic technologies have the potential to address this problem, by virtue of their ability to manipulate and process single-cells in a rapid, controllable and efficient manner. Indeed, when coupled with traditional imaging approaches, microfluidic systems allow the experimentalist to follow single-cell divisions over time. Herein, we present a valve-based microfluidic system able to probe the decision-making processes of single-cells, by tracking their lineage over multiple generations. The system operates by trapping single-cells within growth chambers, allowing the trapped cells to grow and divide, isolating sister cells after a user-defined number of divisions and finally extracting them for downstream transcriptome analysis. The platform incorporates multiple cell manipulation operations, image processing-based automation for cell loading and growth monitoring, reagent addition and device washing. To demonstrate the efficacy of the microfluidic workflow, 6C2 (chicken erythroleukemia) and T2EC (primary chicken erythrocytic progenitors) cells are tracked inside the microfluidic device over two generations, with a cell viability rate in excess of 90%. Sister cells are successfully isolated after division and extracted within a 500 nL volume, which is compatible with downstream single-cell RNA sequencing analysis.

Data provided with the following paper: Richard, A., Boullu, L., Herbach, U., Bonnafoux, A., Morin, V., Vallin, E., Guillemin, A., Papili Gao, N., Gunawan, R., Cosette, J., Arnaud, O., Kupiec, J.-J., Espinasse, T., Gonin-Giraud, D. and Gandrillon, O. (2016). Single-cell-based analysis highlights a surge in cell-to-cell molecular variability preceding irreversible commitment in a differentiation process. Submitted

Additional file 1: Figure S1. [UMAP visualization of scRT-qPCR and scRNAseq data]. Figure S2. [PCA of scRT-qPCR and scRNAseq data]. Figure S3. [Half-life of mRNA table]. Figure S4. [Histograms of viability rate during reversion and differentiation processes]. Figure S5. [Summary table of scRNAseq data filtering steps and threshold values for each step].

Additional file 1: Fig. S1. Single cell transcriptomic landscape of healthy human bone marrow (HBM1). Fig. S2. Strategy used to represent the evolution of cell-to-cell gene expression variability during differentiation. Fig. S3. Evolution of cell-to-cell gene expression variability during the main pathways of normal hematopoietic differentiation (HBM2). Fig. S4. Test of different size for the sliding window. Fig. S5. Strategy used to calculate delta-entropy and delta-expression. Fig. S6. Most delta-entropic and most delta-expressed genes along hematopoietic differentiation (HBM1). Fig. S7. delta-entropic and delta-expressed genes along hematopoietic differentiation (HBM2). Fig. S8. Most delta-entropic and most delta-expressed genes along hematopoietic differentiation (HBM2). Fig. S9. Cell-to-cell gene expression variability of transcription factors belonging to the 1000 most delta entropic genes during Erythropoiesis (HBM1). Fig. S10. Cell-to-cell gene expression variability of transcription factors belonging to the 1000 most delta entropic genes during Granulopoiesis (HBM1). Fig. S11. Cell-to-cell gene expression variability of transcription factors belonging to the 1000 most delta entropic genes during dendritic differentiation (HBM1). Fig. S12. Cell-to-cell gene expression variability of transcription factors belonging to the 1000 most delta entropic genes during B lymphopoiesis (HBM1). Fig. S13. Transcriptional landscape of the HSPC compartment of SF3B1 mutated MDS and healthy elderly subjects. Fig. S14. Expression values of selected marker genes for all cell sub-populations of the HSPC compartment of SF3B1 mutated MDS and healthy elderly subjects. Fig. S15. Evolution of cell-to-cell gene expression variability during granulopoiesis in elderly subjects and SF3B1-mutated MDS. Fig. S16. Evolution of cell-to-cell gene expression variability during dendritic differentiation in elderly subjects and SF3B1-mutated MDS. Fig. S17. Evolution of cell-to-cell gene expression variability during B lymphopoiesis in elderly subjects and SF3B1-mutated MDS. Fig. S18. Evolution of cell-to-cell gene expression variability during Erythropoiesis in elderly subjects and SF3B1-mutated MDS. Fig. S19. Comparison of the 100 most delta entropic genes along hematopoietic differentiation between MDS patients and age-matched healthy subjects. Table S1. Distribution of cellular subpopulations for each differentiation pathway in HBM1 and HBM2 dataset. Table S2. Clinico-biological features of patients from whom HSPCs were harvested for the scRNA-seq experiment. Table S3. Distribution of cellular subpopulations for each differentiation pathway in Ctrl1, Ctrl3, MDS2 and MDS4 samples. Table S4. Common delta-entropic genes between MDS patients and age-matched healthy subjects during the different hematopoietic differentiation pathways.

Motivation Single cell transcriptional profiling opens up a new avenue in studying the functional role of cell-to-cell variability in physiological processes. The analysis of single cell expression profiles creates new challenges due to the distributive nature of the data and the stochastic dynamics of gene transcription process. The reconstruction of gene regulatory networks (GRNs) using single cell transcriptional profiles is particularly challenging, especially when directed gene-gene relationships are desired. Results We developed SINCERITIES (SINgle CEll Regularized Inference using TIme-stamped Expression profileS) for the inference of GRNs from single cell transcriptional profiles. We focused on time-stamped cross-sectional expression data, commonly generated from transcriptional profiling of single cells collected at multiple time points after cell stimulation. SINCERITIES recovers directed regulatory relationships among genes by employing regularized linear regression (ridge regression), using temporal changes in the distributions of gene expressions. Meanwhile, the modes of the gene regulations (activation and repression) come from partial correlation analyses between pairs of genes. We demonstrated the efficacy of SINCERITIES in inferring GRNs using in silico time-stamped single cell expression data and single cell transcriptional profiles of THP-1 monocytic human leukemia cells. The case studies showed that SINCERITIES could provide accurate GRN predictions, significantly better than other GRN inference algorithms such as TSNI, GENIE3 and JUMP3. Moreover, SINCERITIES has a low computational complexity and is amenable to problems of extremely large dimensionality. Finally, an application of SINCERITIES to single cell expression data of T2EC chicken erythrocytes pointed to BATF as a candidate novel regulator of erythroid development. Availability and implementation MATLAB and R version of SINCERITIES are freely available from the following websites: http://www.cabsel.ethz.ch/tools/sincerities.html and https://github.com/CABSEL/SINCERITIES. The single cell THP-1 and T2EC transcriptional profiles are available from the original publications (Kouno et al., 2013; Richard et al., 2016). The in silico single cell data are available on SINCERITIES websites. Supplementary information Supplementary data are available at Bioinformatics online.