Srivats Hariharan

Quantitative neurite outgrowth measurement based on image segmentation with topological dependence

The study of neuronal morphology and neurite outgrowth has been enhanced by the combination of imaging informatics and high content screening, in which thousands of images are acquired using robotic fluorescent microscopy. To understand the process of neurite outgrowth in the context of neuroregeneration, we used mouse neuroblastoma N1E115 as our model neuronal cell. Six‐thousand cellular images of four different culture conditions were acquired with two‐channel widefield fluorescent microscopy. We developed a software package called NeuronCyto. It is a fully automatic solution for neurite length measurement and complexity analysis. A novel approach based on topological analysis is presented to segment cells. The detected nuclei were used as references to initialize the level set function. Merging and splitting of cells segments were prevented using dynamic watershed lines based on the constraint of topological dependence. A tracing algorithm was developed to automatically trace neurites and measure their lengths quantitatively on a cell‐by‐cell basis. NeuronCyto analyzes three important biologically relevant features, which are the length, branching complexity, and number of neurites. The application of NeuronCyto on the experiments of Toca‐1 and serum starvation show that the transfection of Toca‐1 cDNA induces longer neurites with more complexities than serum starvation.

Evolving generalized Voronoi diagrams for accurate cellular image segmentation

Analyzing cellular morphologies on a cell‐by‐cell basis is vital for drug discovery, cell biology, and many other biological studies. Interactions between cells in their culture environments cause cells to touch each other in acquired microscopy images. Because of this phenomenon, cell segmentation is a challenging task, especially when the cells are of similar brightness and of highly variable shapes. The concept of topological dependence and the maximum common boundary (MCB) algorithm are presented in our previous work (Yu et al., Cytometry Part A 2009;75A:289–297). However, the MCB algorithm suffers a few shortcomings, such as low computational efficiency and difficulties in generalizing to higher dimensions. To overcome these limitations, we present the evolving generalized Voronoi diagram (EGVD) algorithm. Utilizing image intensity and geometric information, EGVD preserves topological dependence easily in both 2D and 3D images, such that touching cells can be segmented satisfactorily. A systematic comparison with other methods demonstrates that EGVD is accurate and much more efficient.

Identification of ApoE as an autocrine/paracrine factor that stimulates neural stem cell survival via MAPK/ERK signaling pathway

J. Neurochem. (2011) 117, 565–578.

Level Set Segmentation of Cellular Images Based on Topological Dependence

Segmentation of cellular images presents a challenging task for computer vision, especially when the cells of irregular shapes clump together. Level set methods can segment cells with irregular shapes when signal-to-noise ratio is low, however they could not effectively segment cells that are clumping together. We perform topological analysis on the zero level sets to enable effective segmentation of clumped cells. Geometrical shapes and intensities are important information for segmentation of cells. We assimilated them in our approach and hence we are able to gain from the advantages of level sets while circumventing its shortcoming. Validation on a data set of 4916 neural cells shows that our method is 93.3 ±0.6% accurate.

Single-cell mRNA profiling identifies progenitor subclasses in neurospheres.

Neurospheres are widely used to propagate and investigate neural stem cells (NSCs) and neural progenitors (NPs). However, the exact cell types present within neurospheres are still unknown. To identify cell types, we used single-cell mRNA profiling of 48 genes in 187 neurosphere cells. Using a clustering algorithm, we identified 3 discrete cell populations within neurospheres. One cell population [cluster unsorted (US) 1] expresses high Bmi1 and Hes5 and low Myc and Klf12. Cluster US2 shows intermediate expression of most of the genes analyzed. Cluster US3 expresses low Bmi1 and Hes5 and high Myc and Klf12. The mRNA profiles of these 3 cell populations correlate with a developmental timeline of early, intermediate, and late NPs, as seen in vivo from the mouse brain. We enriched the cell population for neurosphere-forming cells (NFCs) using morphological criteria of forward scatter (FSC) and side scatter (SSC). FSC/SSC(high) cells generated 2.29-fold more neurospheres than FSC/SSC(low) cells at clonal density. FSC/SSC(high) cells were enriched for NSCs and Lewis-X(+ve) cells, possessed higher phosphacan levels, and were of a larger cell size. Clustering of both FSC/SSC(high) and FSC/SSC(low) cells identified an NFC cluster. Significantly, the mRNA profile of the NFC cluster drew close resemblance to that of early NPs. Taken together, data suggest that the neurosphere culture system can be used to model central nervous system development, and that early NPs are the cell population that gives rise to neurospheres. In future work, it may be possible to further dissect the NFCs and reveal the molecular signature for NSCs.

Online 3-D Tracking of Suspension Living Cells Imaged with Phase-Contrast Microscopy

Neural stem cells/neural progenitors (NSCs/NPs) are cells that give rise to the main cell types of the nervous system: oligodendrocytes, neurons, and astrocytes. Studying NSCs/NPs with time-lapse microscopy is critical to the understanding of the biology of these cells. However, NSCs/NPs are very sensitive to phototoxic damage, and therefore, fluorescent dyes cannot be used to follow these cells. Also, since in most of NSC/NP-related experiments, a large number of cells neesd to be monitored. Consequently, the acquisition of a huge amount of images is required. An additional difficulty is related to our original suspension living, tracking objective, behavior much closer to the natural, in vivo, way of development of the cells. Indeed, unlike adherent cells, suspension cells float freely in a liquid solution, thus, making their dynamics very different from that of adherent cells. As a result, existing visual tracking algorithms that have primarily been developed to track adherent cells are no longer adequate to tackle living cells in suspension. This paper presents a novel automated 3-D visual tracking of suspension living cells for time-lapse image acquisition using phase-contrast microscopy. This new tracking method can potentially strongly impact on current 3-D video microscopy methods, paving the way for innovative analysis of NSCs/NPs and as a result, on the study of neurodegenerative diseases.

Investigating the Spatial Distribution of Integrin β1 in Patterned Human Mesenchymal Stem Cells Using Super-Resolution Imaging

Lineage commitment of human mesenchymal stem cells (hMSCs) could be directed through micro/nanopatterning of the extracellular matrix (ECM) between cells and substrate. Integrin receptors, integrator of the ECM and cell cytoskeleton, function as molecular bridges linking cells to different biophysical cues translated from patterned ECM. Here we report the distinct recruitment of active integrin β1 (ITG-β1) in hMSCs when they were committed toward the cardiomyogenic lineage on a micropatterned surface. In addition, a systematic study of the distribution of ITG-β1 was performed on focal adhesions (FAs) using a direct stochastic optical reconstruction microscopy (dSTORM) technique, a super-resolution imaging technique to establish the relationship between types of integrin expression and its distribution pattern that are associated with cardiomyogenic differentiation of hMSCs. We ascertained that elongated FAs of ITG-β1 expressed in patterned hMSCs were more prominent than FAs expressed in unpatterned hMSCs. However, there was no significant difference observed between the widths of FAs from both experimental groups. It was found in patterned hMSCs that the direction of FA elongation coincides with cell orientation. This phenomenon was however not observed in unpatterned hMSCs. These results showed that the biophysical induction methods like FAs patterning could selectively induce hMSCs lineage commitment via integrin-material interaction.

A fluorescent probe for imaging symmetric and asymmetric cell division in neurosphere formation.

We report here a novel fluorescent chemical probe which stains distinct neural stem/progenitor cells (NSPCs) by binding to acid ceramidase in mouse neurospheres. is distributed evenly or unevenly to the daughter cells during multiple mitoses enabling the live imaging of symmetric and asymmetric divisions of isolated NSPCs.

Enumeration of Neural Stem Cells Using Clonal Assays

Neural stem cells (NSCs) have the ability to self-renew and generate the three major neural lineages — astrocytes, neurons and oligodendrocytes. NSCs and neural progenitors (NPs) are commonly cultured in vitro as neurospheres. This protocol describes in detail how to determine the NSC frequency in a given cell population under clonal conditions. The protocol begins with the seeding of the cells at a density that allows for the generation of clonal neurospheres. The neurospheres are then transferred to chambered coverslips and differentiated under clonal conditions in conditioned medium, which maximizes the differentiation potential of the neurospheres. Finally, the NSC frequency is calculated based on neurosphere formation and multipotency capabilities. Utilities of this protocol include the evaluation of candidate NSC markers, purification of NSCs, and the ability to distinguish NSCs from NPs. This method takes 13 days to perform, which is much shorter than current methods to enumerate NSC frequency.

A field theoretical restoration method for images degraded by non-uniform light attenuation : an application for light microscopy

Microscopy has become a de facto tool for biology. However, it suffers from a fundamental problem of poor contrast with increasing depth, as the illuminating light gets attenuated and scattered and hence can not penetrate through thick samples. The resulting decay of light intensity due to attenuation and scattering varies exponentially across the image. The classical space invariant deconvolution approaches alone are not suitable for the restoration of uneven illumination in microscopy images. In this paper, we present a novel physics-based field theoretical approach to solve the contrast degradation problem of light microscopy images. We have confirmed the effectiveness of our technique through simulations as well as through real field experimentations.