Tissue-engineered fibrin scaffolds containing neural progenitors enhance functional recovery in a subacute model of SCI

Two recurring problems with stem/neural progenitor cell (NPC) transplantation therapies for spinal cord injury (SCI) are poor cell survival and uncontrolled cell differentiation. The current study evaluated the viability and differentiation of embryonic stem cell-derived neural progenitor cells (ESNPCs) transplanted within fibrin scaffolds containing growth factors (GFs) and a heparin-binding delivery system (HBDS) to enhance cell survival and direct differentiation into neurons. Mouse ESNPCs were generated from mouse embryonic stem cells (ESCs) using a 4-/4+ retinoic acid (RA) induction protocol that resulted in a population of cells that was 70% nestin positive NPCs. The ESNPCs were transplanted directly into a rat subacute dorsal hemisection lesion SCI model. ESNPCs were either encapsulated in a fibrin scaffold; encapsulated in fibrin containing the HBDS, neurotrophin-3 (NT-3) and platelet derived growth factor (PDGF-AA); or encapsulated in fibrin scaffolds with NT-3 and PDGF-AA without the HBDS. We report that the combination of GFs and fibrin scaffold (without HBDS) enhanced the total number of ESNPCs present in the treated spinal cords and increased the number of ESNPC-derived NeuN positive neurons 8 weeks after transplantation. All experimental groups treated with ESNPCs exhibited an increase in behavioral function 4 weeks after transplantation. In a subset of animals, the ESNPCs over-proliferated as evidenced by SSEA-1 positive/Ki67 positive ESCs found at 4 and 8 weeks. These results demonstrate the potential of tissue-engineered fibrin scaffolds to enhance the survival of NPCs and highlight the need to purify cell populations used in therapies for SCI.     

Tumor classification based on non-negative matrix factorization using gene expression data

This paper presents a new method for tumor classification using gene expression data. In the proposed method, we first select genes using nonnegative matrix factorization (NMF) or sparse NMF (SNMF), and then we extract features from the selected genes by virtue of NMF or SNMF. At last, we apply support vector machines (SVM) to classify the tumor samples using the extracted features. In order for a better classification, a modified SNMF algorithm is also proposed. The experimental results on benchmark three microarray data sets validate that the proposed method is efficient. Moreover, the biological meaning of the selected genes are also analyzed.