The gene expression of human embryonic stem cells (hESC) is a critical aspect for understanding the normal and pathological development of human cells and tissues. Current bulk gene expression assays rely on RNA extracted from cell and tissue samples with various degree of cellular heterogeneity. These ‘cell population averaging’ data are difficult to interpret, especially for the purpose of understanding the regulatory relationship of genes in the earliest phases of development and differentiation of individual cells. Here, we report a microfluidic approach that can extract total mRNA from individual single-cells and synthesize cDNA on the same device with high mRNA-to-cDNA efficiency. This feature makes large-scale single-cell gene expression profiling possible. Using this microfluidic device, we measured the absolute numbers of mRNA molecules of three genes (B2M, Nodal and Fzd4) in a single hESC. Our results indicate that gene expression data measured from cDNA of a cell population is not a good representation of the expression levels in individual single cells. Within the G0/G1 phase pluripotent hESC population, some individual cells did not express all of the 3 interrogated genes in detectable levels. Consequently, the relative expression levels, which are broadly used in gene expression studies, are very different between measurements from population cDNA and single-cell cDNA. The results underscore the importance of discrete single-cell analysis, and the advantages of a microfluidic approach in stem cell gene expression studies.

We report a high speed and high purity pulsed laser triggered fluorescence activated cell sorter (PLACS) with a sorting throughput up to 20 000 mammalian cells s⁻¹ with 37% sorting purity, 90% cell viability in enrichment mode, and >90% purity in high purity mode at 1500 cells s⁻¹ or 3000 beads s⁻¹. Fast switching (30 μs) and a small perturbation volume (∼90 pL) is achieved by a unique sorting mechanism in which explosive vapor bubbles are generated using focused laser pulses in a single layer microfluidic PDMS channel.

Accurate prognosis of patients with cancer is important for the stratification of patients, the optimization of treatment strategies, and the design of clinical trials. Both clinical features and molecular data can be used for this purpose, for instance, to predict the survival of patients censored at specific time points. Multi-omics data, including genome-wide gene expression, methylation, protein expression, copy number alteration, and somatic mutation data, are becoming increasingly common in cancer studies. To harness the rich information in multi-omics data, we developed GDP (Group lass regularized Deep learning for cancer Prognosis), a computational tool for survival prediction using both clinical and multi-omics data. GDP integrated a deep learning framework and Cox proportional hazard model (CPH) together, and applied group lasso regularization to incorporate gene-level group prior knowledge into the model training process. We evaluated its performance in both simulated and real data from The Cancer Genome Atlas (TCGA) project. In simulated data, our results supported the importance of group prior information in the regularization of the model. Compared to the standard lasso regularization, we showed that group lasso achieved higher prediction accuracy when the group prior knowledge was provided. We also found that GDP performed better than CPH for complex survival data. Furthermore, analysis on real data demonstrated that GDP performed favorably against other methods in several cancers with large-scale omics data sets, such as glioblastoma multiforme, kidney renal clear cell carcinoma, and bladder urothelial carcinoma. In summary, we demonstrated that GDP is a powerful tool for prognosis of patients with cancer, especially when large-scale molecular features are available.

The hematopoietic stem cell (HSC) compartment is composed of long-term reconstituting (LTR) and short-term reconstituting (STR) stem cells. LTR HSC can reconstitute the hematopoietic system for life, whereas STR HSC can sustain hematopoiesis for only a few weeks in the mouse. Several excellent gene expression profiles have been obtained of the total hematopoietic stem cell population. We have used five-color FACS sorting to isolate separate populations of LTR and STR stem cell subsets. The LTR HSC has the phenotype defined as Lin^-Sca⁺Kit⁺38⁺34^-; two subsets of STR HSC were obtained with phenotypes of Lin^-Sca⁺Kit⁺38⁺34⁺and Lin^-Sca⁺Kit⁺38^-34⁺. The microarray profiling study reported here was able to identify genes specific for LTR functions. In the interrogated genes (≈12,000 probe sets corresponding to 8,000 genes), 210 genes are differentially expressed, and 72 genes are associated with LTR activity, including membrane proteins, signal transduction molecules, and transcription factors. Hierarchical clustering of the 210 differentially expressed genes suggested that they are not bone marrow-specific but rather appear to be stem cell-specific. Transcription factor-binding site analysis suggested that GATA3 might play an important role in the biology of LTR HSC.

Acute lymphoblastic leukemia (ALL) has been generally considered a genetic disease (disorder) with an aggressive tumor entity of highly proliferative malignant lymphoid cells. However, in recent years, significant advances have been made in the elucidation of the ALL-associated processes. Thus, we understand that histone acetylation is involved in the permanent changes of gene expression controlling ALL developmental outcomes. In this article, we will focus on histone acetylation associated with ALL, their implications as biomarkers for prognostic, and their preclinical and clinical applications.