Marc Hulsman

An algorithm-based topographical biomaterials library to instruct cell fate

It is increasingly recognized that material surface topography is able to evoke specific cellular responses, endowing materials with instructive properties that were formerly reserved for growth factors. This opens the window to improve upon, in a cost-effective manner, biological performance of any surface used in the human body. Unfortunately, the interplay between surface topographies and cell behavior is complex and still incompletely understood. Rational approaches to search for bioactive surfaces will therefore omit previously unperceived interactions. Hence, in the present study, we use mathematical algorithms to design nonbiased, random surface features and produce chips of poly(lactic acid) with 2,176 different topographies. With human mesenchymal stromal cells (hMSCs) grown on the chips and using high-content imaging, we reveal unique, formerly unknown, surface topographies that are able to induce MSC proliferation or osteogenic differentiation. Moreover, we correlate parameters of the mathematical algorithms to cellular responses, which yield novel design criteria for these particular parameters. In conclusion, we demonstrate that randomized libraries of surface topographies can be broadly applied to unravel the interplay between cells and surface topography and to find improved material surfaces.

Somatic mutations found in the healthy blood compartment of a 115-yr-old woman demonstrate oligoclonal hematopoiesis

The somatic mutation burden in healthy white blood cells (WBCs) is not well known. Based on deep whole-genome sequencing, we estimate that approximately 450 somatic mutations accumulated in the nonrepetitive genome within the healthy blood compartment of a 115-yr-old woman. The detected mutations appear to have been harmless passenger mutations: They were enriched in noncoding, AT-rich regions that are not evolutionarily conserved, and they were depleted for genomic elements where mutations might have favorable or adverse effects on cellular fitness, such as regions with actively transcribed genes. The distribution of variant allele frequencies of these mutations suggests that the majority of the peripheral white blood cells were offspring of two related hematopoietic stem cell (HSC) clones. Moreover, telomere lengths of the WBCs were significantly shorter than telomere lengths from other tissues. Together, this suggests that the finite lifespan of HSCs, rather than somatic mutation effects, may lead to hematopoietic clonal evolution at extreme ages.

A mesenchymal stromal cell gene signature for donor age

Human aging is associated with loss of function and regenerative capacity. Human bone marrow derived mesenchymal stromal cells (hMSCs) are involved in tissue regeneration, evidenced by their capacity to differentiate into several lineages and therefore are considered the golden standard for cell-based regeneration therapy. Tissue maintenance and regeneration is dependent on stem cells and declines with age and aging is thought to influence therapeutic efficacy, therefore, more insight in the process of aging of hMSCs is of high interest. We, therefore, hypothesized that hMSCs might reflect signs of aging. In order to find markers for donor age, early passage hMSCs were isolated from bone marrow of 61 donors, with ages varying from 17–84, and clinical parameters, in vitro characteristics and microarray analysis were assessed. Although clinical parameters and in vitro performance did not yield reliable markers for aging since large donor variations were present, genome-wide microarray analysis resulted in a considerable list of genes correlating with human age. By comparing the transcriptional profile of aging in human with the one from rat, we discovered follistatin as a common marker for aging in both species. The gene signature presented here could be a useful tool for drug testing to rejuvenate hMSCs or for the selection of more potent, hMSCs for cell-based therapy.

Predicting the therapeutic efficacy of MSC in bone tissue engineering using the molecular marker CADM1

Mesenchymal stromal cells (hMSCs) are advancing into the clinic but the therapeutic efficacy of hMSCs faces the problem of donor variability. In bone tissue engineering, no reliable markers have been identified which are able to predict the bone-forming capacity of hMSCs prior to implantation. To this end, we isolated hMSCs from 62 donors and characterized systematically their in vitro lineage differentiation capacity, gene expression signature and in vivo capacity for ectopic bone formation. Our data confirms the large variability of in vitro differentiation capacity which did not correlate with in vivo ectopic bone formation. Using DNA microarray analysis of early passage hMSCs we identified a diagnostic bone-forming classifier. In fact, a single gene, CADM1, strongly correlated with the bone-forming capacity of hMSCs and could be used as a reliable in vitro diagnostic marker. Furthermore, data mining of genes expressed correlating with in vivo bone formation represented involvement in neurogenic processes and Wnt signaling. We will apply our data set to predict therapeutic efficacy of hMSCs and to gain novel insight in the process of bone regeneration. Our bio-informatics driven approach may be used in other fields of cell therapy to establish diagnostic markers for clinical efficacy.

Analysis of high-throughput screening reveals the effect of surface topographies on cellular morphology

Surface topographies of materials considerably impact cellular behavior as they have been shown to affect cell growth, provide cell guidance, and even induce cell differentiation. Consequently, for successful application in tissue engineering, the contact interface of biomaterials needs to be optimized to induce the required cell behavior. However, a rational design of biomaterial surfaces is severely hampered because knowledge is lacking on the underlying biological mechanisms. Therefore, we previously developed a high-throughput screening device (TopoChip) that measures cell responses to large libraries of parameterized topographical material surfaces. Here, we introduce a computational analysis of high-throughput materiome data to capture the relationship between the surface topographies of materials and cellular morphology. We apply robust statistical techniques to find surface topographies that best promote a certain specified cellular response. By augmenting surface screening with data-driven modeling, we determine which properties of the surface topographies influence the morphological properties of the cells. With this information, we build models that predict the cellular response to surface topographies that have not yet been measured. We analyze cellular morphology on 2176 surfaces, and find that the surface topography significantly affects various cellular properties, including the roundness and size of the nucleus, as well as the perimeter and orientation of the cells. Our learned models capture and accurately predict these relationships and reveal a spectrum of topographies that induce various levels of cellular morphologies. Taken together, this novel approach of high-throughput screening of materials and subsequent analysis opens up possibilities for a rational design of biomaterial surfaces.

Meta- analysis on blood transcriptomic studies identifies consistently coexpressed protein- protein interaction modules as robust markers of human aging

The bodily decline that occurs with advancing age strongly impacts on the prospects for future health and life expectancy. Despite the profound role of age in disease etiology, knowledge about the molecular mechanisms driving the process of aging in humans is limited. Here, we used an integrative network‐based approach for combining multiple large‐scale expression studies in blood (2539 individuals) with protein–protein Interaction (PPI) data for the detection of consistently coexpressed PPI modules that may reflect key processes that change throughout the course of normative aging. Module detection followed by a meta‐analysis on chronological age identified fifteen consistently coexpressed PPI modules associated with chronological age, including a highly significant module (P = 3.5 × 10−38) enriched for ‘T‐cell activation’ marking age‐associated shifts in lymphocyte blood cell counts (R2 = 0.603; P = 1.9 × 10−10). Adjusting the analysis in the compendium for the ‘T‐cell activation’ module showed five consistently coexpressed PPI modules that robustly associated with chronological age and included modules enriched for ‘Translational elongation’, ‘Cytolysis’ and ‘DNA metabolic process’. In an independent study of 3535 individuals, four of five modules consistently associated with chronological age, underpinning the robustness of the approach. We found three of five modules to be significantly enriched with aging‐related genes, as defined by the GenAge database, and association with prospective survival at high ages for one of the modules including ASF1A. The hereby‐detected age‐associated and consistently coexpressed PPI modules therefore may provide a molecular basis for future research into mechanisms underlying human aging.

Detecting recurrent gene mutation in interaction network context using multi-scale graph diffusion

BackgroundDelineating the molecular drivers of cancer, i.e. determining cancer genes and the pathways which they deregulate, is an important challenge in cancer research. In this study, we aim to identify pathways of frequently mutated genes by exploiting their network neighborhood encoded in the protein-protein interaction network. To this end, we introduce a multi-scale diffusion kernel and apply it to a large collection of murine retroviral insertional mutagenesis data. The diffusion strength plays the role of scale parameter, determining the size of the network neighborhood that is taken into account. As a result, in addition to detecting genes with frequent mutations in their genomic vicinity, we find genes that harbor frequent mutations in their interaction network context.ResultsWe identify densely connected components of known and putatively novel cancer genes and demonstrate that they are strongly enriched for cancer related pathways across the diffusion scales. Moreover, the mutations in the clusters exhibit a significant pattern of mutual exclusion, supporting the conjecture that such genes are functionally linked. Using multi-scale diffusion kernel, various infrequently mutated genes are found to harbor significant numbers of mutations in their interaction network neighborhood. Many of them are well-known cancer genes.ConclusionsThe results demonstrate the importance of defining recurrent mutations while taking into account the interaction network context. Importantly, the putative cancer genes and networks detected in this study are found to be significant at different diffusion scales, confirming the necessity of a multi-scale analysis.

Exploring Sequence Characteristics Related to High-Level Production of Secreted Proteins in Aspergillus niger

Protein sequence features are explored in relation to the production of over-expressed extracellular proteins by fungi. Knowledge on features influencing protein production and secretion could be employed to improve enzyme production levels in industrial bioprocesses via protein engineering. A large set, over 600 homologous and nearly 2,000 heterologous fungal genes, were overexpressed in Aspergillus niger using a standardized expression cassette and scored for high versus no production. Subsequently, sequence-based machine learning techniques were applied for identifying relevant DNA and protein sequence features. The amino-acid composition of the protein sequence was found to be most predictive and interpretation revealed that, for both homologous and heterologous gene expression, the same features are important: tyrosine and asparagine composition was found to have a positive correlation with high-level production, whereas for unsuccessful production, contributions were found for methionine and lysine composition. The predictor is available online at http://bioinformatics.tudelft.nl/hipsec. Subsequent work aims at validating these findings by protein engineering as a method for increasing expression levels per gene copy.

Hi-C Chromatin Interaction Networks Predict Co-expression in the Mouse Cortex

The three dimensional conformation of the genome in the cell nucleus influences important biological processes such as gene expression regulation. Recent studies have shown a strong correlation between chromatin interactions and gene co-expression. However, predicting gene co-expression from frequent long-range chromatin interactions remains challenging. We address this by characterizing the topology of the cortical chromatin interaction network using scale-aware topological measures. We demonstrate that based on these characterizations it is possible to accurately predict spatial co-expression between genes in the mouse cortex. Consistent with previous findings, we find that the chromatin interaction profile of a gene-pair is a good predictor of their spatial co-expression. However, the accuracy of the prediction can be substantially improved when chromatin interactions are described using scale-aware topological measures of the multi-resolution chromatin interaction network. We conclude that, for co-expression prediction, it is necessary to take into account different levels of chromatin interactions ranging from direct interaction between genes (i.e. small-scale) to chromatin compartment interactions (i.e. large-scale).

Unbiased Quantitative Models of Protein Translation Derived from Ribosome Profiling Data

Translation of RNA to protein is a core process for any living organism. While for some steps of this process the effect on protein production is understood, a holistic understanding of translation still remains elusive. In silico modelling is a promising approach for elucidating the process of protein synthesis. Although a number of computational models of the process have been proposed, their application is limited by the assumptions they make. Ribosome profiling (RP), a relatively new sequencing-based technique capable of recording snapshots of the locations of actively translating ribosomes, is a promising source of information for deriving unbiased data-driven translation models. However, quantitative analysis of RP data is challenging due to high measurement variance and the inability to discriminate between the number of ribosomes measured on a gene and their speed of translation. We propose a solution in the form of a novel multi-scale interpretation of RP data that allows for deriving models with translation dynamics extracted from the snapshots. We demonstrate the usefulness of this approach by simultaneously determining for the first time per-codon translation elongation and per-gene translation initiation rates of Saccharomyces cerevisiae from RP data for two versions of the Totally Asymmetric Exclusion Process (TASEP) model of translation. We do this in an unbiased fashion, by fitting the models using only RP data with a novel optimization scheme based on Monte Carlo simulation to keep the problem tractable. The fitted models match the data significantly better than existing models and their predictions show better agreement with several independent protein abundance datasets than existing models. Results additionally indicate that the tRNA pool adaptation hypothesis is incomplete, with evidence suggesting that tRNA post-transcriptional modifications and codon context may play a role in determining codon elongation rates.