Computational prediction of protein interactions typically use protein domains as classifier features because they capture conserved information of interaction surfaces. However, approaches relying on domains as features cannot be applied to proteins without any domain information. In this paper, we explore the contribution of pure amino acid composition (AAC) for protein interaction prediction. This simple feature, which is based on normalized counts of single or pairs of amino acids, is applicable to proteins from any sequenced organism and can be used to compensate for the lack of domain information.

DNA aptamers generated by cell-SELEX offer an attractive alternative to antibodies, but generating aptamers to specific, known membrane protein targets has proven challenging, and has severely limited the use of aptamers as affinity reagents for cell identification and purification.

Constructing coexpression networks and performing network analysis using large-scale gene expression data sets is an effective way to uncover new biological knowledge; however, the methods used for gene association in constructing these coexpression networks have not been thoroughly evaluated. Since different methods lead to structurally different coexpression networks and provide different information, selecting the optimal gene association method is critical.

Dormant hematopoietic stem cells (HSCs) are activated by microenvironmental cues of the niche in response to the injury of bone marrow (BM). It is not clearly understood how engrafted cells respond to these cues and are involved in marrow regeneration. The purpose of this study was to decipher this cellular response in competitive environment. BM cells of CD45.2 mice were transplanted in sub-lethally irradiated CD45.1 mice. The status of the donor and recipient stem cells (LSK: Lin(-)Sca-1(+)c-Kit(+)) were determined by flowcytometry using CD45 alleles specific antibodies. The presence of long-term engraftable stem cells was confirmed by marrow repopulation assay in secondary hosts, and cell cycle status was determined by staining with Ho33342 and pyronin Y, and BrdU retention assay. The expressions of different hematopoietic growth factor genes in stromal compartment (CD45(-) cells) were assessed by real-time reverse transcriptase- polymerase chain reaction (RT-PCR). The presence of donor cells initially stimulated the proliferation of host LSK cells compared with control mice without transplantation. This was expected due to pro-mitotic and anti-apoptotic factors secreted by the donor hematopoietic cells. Upon transplantation, a majority of the donor LSK cells entered into cell cycle, and later they maintained cell cycle status similar to that in the normal mouse. Donor-derived LSK cells showed 1000-fold expansion within 15 days of transplantation. Donor-derived cells not only regenerated BM in the primary irradiated host for long-term, they were also found to be significantly involved in marrow regeneration after the second cycle of irradiation. The proliferation of LSK cells was associated with the onset of colossal expression of different hematopoietic growth factor genes in non-hematopoietic cellular compartment. Activation of donor LSK cells was found to be dynamically controlled by BM cellularity. Long-term study showed that a high level of hematopoietic reconstitution could be possible by donor cells in a sub-lethally irradiated host.

As newer single-cell protocols generate increasingly more cells at reduced sequencing depths, the value of a higher read depth may be overlooked. Using data from three different single-cell RNA-seq protocols that lend themselves to having either higher read depth (Smart-seq) or many cells (MARS-seq and 10X), we evaluate their ability to recapitulate biological signals in the context of spatial reconstruction. Overall, we find gene expression profiles after spatial reconstruction analysis are highly reproducible between datasets despite being generated by different protocols and using different computational algorithms. While UMI-based protocols such as 10X and MARS-seq allow for capturing more cells, Smart-seq's higher sensitivity and read-depth allow for analysis of lower expressed genes and isoforms. Additionally, we evaluate trade-offs for each protocol by performing subsampling analyses and find that optimizing the balance between sequencing depth and number of cells within a protocol is necessary for efficient use of resources. Our analysis emphasizes the importance of selecting a protocol based on the biological questions and features of interest.