SCLD: a Stem Cell Lineage Database for the annotation of cell types and developmental lineages.

Stem cell biology has experienced explosive growth over the past decade as researchers attempt to generate therapeutically relevant cell types in the laboratory. Recapitulation of endogenous developmental trajectories is a dominant paradigm in the design of directed differentiation protocols, and attempts to guide stem cell differentiation are often based explicitly on knowledge of in vivo development. Therefore, when designing protocols, stem cell biologists rely heavily upon information including (i) cell type-specific gene expression profiles, (ii) anatomical and developmental relationships between cells and tissues and (iii) signals important for progression from progenitors to target cell types.

Single-cell transcript analysis of human embryonic stem cells.

We demonstrate the qualitative and quantitative power of single-cell transcript analysis to characterize transcriptome dynamics in human embryonic stem cells (hESC's). Single-cell analysis can systematically determine unique cellular profiles for use in cell sorting and identification, show the potential to augment standing models of cellular differentiation, and elucidate the behavior of stem cells exiting pluripotency. Using single-cell analysis of H9 hESC's differentiating under three culture conditions, we revealed transient expression of mesendodermal markers in all three protocols, followed by increasingly stable expression of embryonic endoderm and extra-embryonic endoderm markers.

ES cell cycle progression and differentiation require the action of the histone methyltransferase Dot1L.

Mouse embryonic stem cells (ESCs) proliferate with rapid cell cycle kinetics but without loss of pluripotency. The histone methyltransferase Dot1L is responsible for methylation of histone H3 at lysine 79 (H3K79me). We investigated whether ESCs require Dot1L for proper stem cell behavior.

Characterization of transcript levels for matrix molecules and proteases in ruptured human anterior cruciate ligaments.

An improved understanding of cellular responses during normal anterior cruciate ligament (ACL) function or repair is essential for clinical assessments, understanding ligament biology, and the implementation of tissue engineering strategies. The present study utilized quantitative real-time RT-PCR combined with univariate and multivariate statistical analyses to establish a quantitative database of marker transcript expression that can provide a "blueprint" of ACL wound healing. Selected markers (collagen types I and III, biglycan, decorin, MMP-1, MMP-2, MMP-9, and TIMP-1) were assessed from 33 torn ACLs harvested during reconstructive surgery.