Search

Neural stem cells (NSCs) show great promise in drug discovery and clinical application. Yet few efforts have been made to optimize biocompatible materials for such cells to be expanded and used in clinical conditions. We have previously demonstrated that NSCs are readily cultured on substrates of certain recombinant spider silk protein without addition of animal- or human-derived components. The question remains however whether this material allows differentiation into functional neurons, and whether such differentiation can take place also when the NSCs are cultured not only upon but also within the biodegradable material. Here we demonstrate that "foam"-like structures generated from recombinant spider silk protein (4RepCT) provided excellent matrices for the generation and multicellular analysis of functional excitatory neurons from NSCs without addition of animal- or human-derived components. NSCs isolated from the cerebral cortices of rat embryos were cultured at either 4RepCT matrices shaped as foam-like structures without coating, or on conventional polystyrene plates coated with poly-L-ornithine and fibronectin. Upon treatment with recombinant proteins including the extracellular signaling factor BMP4 or a combination of BMP4 and the signaling factor Wnt3a, the cortical NSCs cultured in 4RepCT foam-like structures differentiated efficiently into neurons that responded to glutamate receptor agonists, such as AMPA, to the same extent as control cultures. Matrices derived from recombinant spider silk proteins thus provide a functional microenvironment for neural stem cells with little or no animal- or human-derived components and can be employed in the development of new strategies in stem cell research and tissue engineering.

Pseudokinases, comprising 10% of the human kinome, are emerging as regulators of canonical kinases and their functions are starting to be defined. We previously identified the pseudokinase Nuclear Receptor Binding Protein 2 (NRBP2) in a screen for genes regulated during neural differentiation. During mouse brain development, NRBP2 is expressed in the cerebellum, and in the adult brain, mainly confined to specific neuronal populations. To study the role of NRBP2 in brain tumors, we stained a brain tumor tissue array for NRPB2, and find its expression to be low, or absent, in a majority of the tumors. This includes medulloblastoma (MB), a pediatric tumor of the cerebellum. Using database mining of published MB data sets, we also find that NRBP2 is expressed at a lower level in MB than in the normal cerebellum. Recent studies indicate that MB exhibits frequent epigenetic alternations and we therefore treated MB cell lines with drugs inhibiting DNA methylation or histone deacetylation, which leads to an upregulation of NRBP2 mRNA expression, showing that it is under epigenetic regulation in cultured MB cells. Furthermore, forced overexpression of NRBP2 in MB cell lines causes a dramatic decrease in cell numbers, increased cell death, impaired cell migration and inhibited cell invasion in vitro. Taken together, our data indicate that downregulation of NRBP2 may be a feature by which MB cells escape growth regulation.

Figure S5. Total illumination energy load in LSTM vs. LSM. The schematic summarizes the calculations of total energy loads imparted in LSTM and LSM for imaging of a sample of specific dimensions, imaged with a specific detection objective. (a) In LSTM, a horizontal plane across the entire sample is imaged with approximately non-overlapping thin sheets of light. Therefore, total energy load can be calculated by step-wise scanning of the sample (for each plane) through the illuminating light. For each of the steps, all voxels that receive light are incremented by 1. The procedure was implemented for a range of parameters and two detection objectives (10Ă /0.6NA/8mmWD and 25/1.0NA/8mmWD). (b) In LSM a stack (or tile) is acquired by approximately non-overlapping thin sheets of light. The total energy load is calculated by summing up the illumination for all tiles in a row along the width. Note that the dwell time of illumination line profile is same for both LSTM and LSM (scanned light sheet implementation, e.g., COLM). The energy load for tiles along the sample length scales up by the same constant factor in LSTM and LSM; therefore, we only simulated one row of tiles along the sample width. (PDF 556 kb)

Figure S1. LSTM microscopy implementation. (a) Image of the physical LSTM setup. (b) 3D model of LSTM and the sample mounting system. The 3D-printed sample chamber is designed to accommodate large biological samples of virtually any dimensions, while still allowing the objectives to be immersed in the immersion oil. Two transparent glass windows, located on the lateral sides, provide visual view of the sample for ease of positioning. An additional window is realized at the bottom part of the chamber to allow the illumination light to pass through. An additional adapter was designed to allow mounting a prism mirror at about approximately 10° from the normal surface to facilitate the optical alignment of the system. (PDF 1623 kb)

Figure S4. Comparison of maximum illumination path lengths in LSTM and LSM. The graphs plot the binarized ratios ( w / t cos θ $$ w/\left(\frac{t}{\cos \left(\uptheta \right)}\right) $$ ) of maximum illumination path lengths required for complete coverage of samples of various widths (w) and thicknesses (t) for different angular arrangements. Magenta and cyan regions mark the combinations of w and t for which the illumination path lengths were smaller in LSTM and LSM, respectively. (PDF 262 kb)

Figure S3. Physical constraints of positioning illumination and detection objectives. (a) Schematics showing calculations of the elongated working distance (EWD) of the air illumination objective (Olympus Macro 4×/0.28NA/29.5WD Air) when used in immersion liquid (refractive index 1.454). Original working distance (OWD) is the working distance in air according to the objective specifications. A thin quartz coverslip and a 3D-printed cap were used to seal the illumination objectives. EWD was estimated to be 43.84 mm. (b) Geometric constraints calculation for the co-arrangement of the illumination and detection objectives. The two boundary conditions are shown in blue and green shading of the illumination objective. For the upper bound limit (blue), the relationship among different parameters is defined by the equation W 2 ∗ sin θ i = D 1 2 + D 2 ∗ cos θ i 2 $$ W2\ast \sin \left(\uptheta i\right)=\frac{D1}{2}+D2\ast \frac{\cos \left(\uptheta i\right)}{2} $$ . For the lower bound limit (green), it is defined by W 2 ∗ cos θ f = W 1 + D 2 ∗ sin θ f 2 $$ W2\ast \cos \left(\uptheta f\right)=W1+D2\ast \frac{\sin \left(\uptheta f\right)}{2} $$ . W1 and W2 are the effective working distances of detection and illumination objectives respectively. D1 and D2 are the diameters of the detection and illumination objectives respectively. θi and θf are the angular positions of upper and lower bounds respectively. For the 4×/0.28NA/29.5 mmWD (as illumination objective) and 10×/0.6NA/8mmWD (as detection objective), the calculated θi and θf are 43.32° and 63.37° respectively. This range served as a starting point during the optical alignment of the system. (PDF 596 kb)