With age, neural stem cell (NSC) function in the adult ventricular-subventricular zone (V-SVZ) declines, reducing memory and cognitive function in males; however, the impact on females is not well understood. To obtain a global view of how age and sex impact the mouse V-SVZ, we constructed 3D montages after multiplex immunostaining, and used computer-based 3D image analysis to quantify data across the entire niche at 2, 18, and 22 months. We discovered dramatic sex differences in the aging of the V-SVZ niche vasculature, which regulates NSC activity: females showed increased diameter but decreased vessel density with age, while males showed decreased diameter and increased tortuosity and vessel density. Accompanying these vascular changes, males showed significant decline in NSC numbers, progenitor cell proliferation, and more disorganized migrating neuroblast chains with age; however, females did not. By examining the entire 3D niche, we found significant sex differences, with females being relatively spared through very old age.

Neural stem cell activity in the ventricular-subventricular zone (V-SVZ) decreases with aging, thought to occur by a unidirectional decline. However, by analyzing the V-SVZ transcriptome of male mice at 2, 6, 18, and 22 months, we found that most of the genes that change significantly over time show a reversal of trend, with a maximum or minimum expression at 18 months. In vivo, MASH1(+) progenitor cells decreased in number and proliferation between 2 and 18 months but increased between 18 and 22 months. Time-lapse lineage analysis of 944 V-SVZ cells showed that age-related declines in neurogenesis were recapitulated in vitro in clones. However, activated type B/type C cell clones divide slower at 2 to 18 months, then unexpectedly faster at 22 months, with impaired transition to type A neuroblasts. Our findings indicate that aging of the V-SVZ involves significant non-monotonic changes that are programmed within progenitor cells and are observable independent of the aging niche.

Despite the superb fossil record of the saber-toothed cat, Smilodon fatalis, ontogenetic age determination for this and other ancient species remains a challenge. The present study utilizes a new technique, a combination of data from stable oxygen isotope analyses and micro-computed tomography, to establish the eruption rate for the permanent upper canines in Smilodon fatalis. The results imply an eruption rate of 6.0 millimeters per month, which is similar to a previously published average enamel growth rate of the S. fatalis upper canines (5.8 millimeters per month). Utilizing the upper canine growth rate, the upper canine eruption rate, and a previously published tooth replacement sequence, this study calculates absolute ontogenetic age ranges of tooth development and eruption in S. fatalis. The timing of tooth eruption is compared between S. fatalis and several extant conical-toothed felids, such as the African lion (Panthera leo). Results suggest that the permanent dentition of S. fatalis, except for the upper canines, was fully erupted by 14 to 22 months, and that the upper canines finished erupting at about 34 to 41 months. Based on these developmental age calculations, S. fatalis individuals less than 4 to 7 months of age were not typically preserved at Rancho La Brea. On the whole, S. fatalis appears to have had delayed dental development compared to dental development in similar-sized extant felids. This technique for absolute ontogenetic age determination can be replicated in other ancient species, including non-saber-toothed taxa, as long as the timing of growth initiation and growth rate can be determined for a specific feature, such as a tooth, and that growth period overlaps with the development of the other features under investigation.

Although many features of neurogenesis during development and in the adult are intrinsic to the neurogenic cells themselves, the role of the microenvironment is irrefutable. The neurogenic niche is a melting pot of cells and factors that influence CNS development. How do the diverse elements assemble and when? How does the niche change structurally and functionally during embryogenesis and in adulthood? In this review, we focus on the impact of non-neural cells that participate in the neurogenic niche, highlighting how cells of different embryonic origins influence this critical germinal space.

Although universally recognized as the source of cerebrospinal fluid (CSF), the choroid plexus (ChP) has been one of the most understudied tissues in neuroscience. The reasons for this are multiple and varied, including historical perceptions about passive and permissive roles for the ChP, experimental issues, and lack of clinical salience. However, recent work on the ChP and instructive signals in the CSF have sparked new hypotheses about how the ChP and CSF provide unexpected means for regulating nervous system structure and function in health and disease, as well as new ChP-based therapeutic approaches using pluripotent stem cell technology. This minisymposium combines new and established investigators to capture some of the newfound excitement surrounding the ChP-CSF system.

Extended abstract of a paper presented at Microscopy and Microanalysis 2008 in Albuquerque, New Mexico, USA, August 3 – August 7, 2008

An algorithmic information-theoretic method is presented for object-level summarization of meaningful changes in image sequences. Object extraction and tracking data are represented as an attributed tracking graph (ATG). Time courses of object states are compared using an adaptive information distance measure, aided by a closed-form multidimensional quantization. The notion of meaningful summarization is captured by using the gap statistic to estimate the randomness deficiency from algorithmic statistics. The summary is the clustering result and feature subset that maximize the gap statistic. This approach was validated on four bioimaging applications: 1) It was applied to a synthetic data set containing two populations of cells differing in the rate of growth, for which it correctly identified the two populations and the single feature out of 23 that separated them; 2) it was applied to 59 movies of three types of neuroprosthetic devices being inserted in the brain tissue at three speeds each, for which it correctly identified insertion speed as the primary factor affecting tissue strain; 3) when applied to movies of cultured neural progenitor cells, it correctly distinguished neurons from progenitors without requiring the use of a fixative stain; and 4) when analyzing intracellular molecular transport in cultured neurons undergoing axon specification, it automatically confirmed the role of kinesins in axon specification.

Brain structural complexity has confounded prior efforts to extract quantitative image-based measurements. We present a systematic 'divide and conquer' methodology for analyzing three-dimensional (3D) multi-parameter images of brain tissue to delineate and classify key structures, and compute quantitative associations among them. To demonstrate the method, thick ( approximately 100 microm) slices of rat brain tissue were labeled using three to five fluorescent signals, and imaged using spectral confocal microscopy and unmixing algorithms. Automated 3D segmentation and tracing algorithms were used to delineate cell nuclei, vasculature, and cell processes. From these segmentations, a set of 23 intrinsic and 8 associative image-based measurements was computed for each cell. These features were used to classify astrocytes, microglia, neurons, and endothelial cells. Associations among cells and between cells and vasculature were computed and represented as graphical networks to enable further analysis. The automated results were validated using a graphical interface that permits investigator inspection and corrective editing of each cell in 3D. Nuclear counting accuracy was >89%, and cell classification accuracy ranged from 81 to 92% depending on cell type. We present a software system named FARSIGHT implementing our methodology. Its output is a detailed XML file containing measurements that may be used for diverse quantitative hypothesis-driven and exploratory studies of the central nervous system.

Automated segmentation and morphometry of fluorescently labeled cell nuclei in batches of 3D confocal stacks is essential for quantitative studies. Model‐based segmentation algorithms are attractive due to their robustness. Previous methods incorporated a single nuclear model. This is a limitation for tissues containing multiple cell types with different nuclear features. Improved segmentation for such tissues requires algorithms that permit multiple models to be used simultaneously. This requires a tight integration of classification and segmentation algorithms. Two or more nuclear models are constructed semiautomatically from user‐provided training examples. Starting with an initial over‐segmentation produced by a gradient‐weighted watershed algorithm, a hierarchical fragment merging tree rooted at each object is built. Linear discriminant analysis is used to classify each candidate using multiple object models. On the basis of the selected class, a Bayesian score is computed. Fragment merging decisions are made by comparing the score with that of other candidates, and the scores of constituent fragments of each candidate. The overall segmentation accuracy was 93.7% and classification accuracy was 93.5%, respectively, on a diverse collection of images drawn from five different regions of the rat brain. The multi‐model method was found to achieve high accuracy on nuclear segmentation and classification by correctly resolving ambiguities in clustered regions containing heterogeneous cell populations. © 2007 International Society for Analytical Cytology

Brain tissue is the most complex tissue in the mammalian anatomy. It is structurally complex, containing diverse cell types and vasculature with complex spatial relationships. It is also complex in terms of molecular constituents, and their relationship to the structural constituents. There is a need for technologies to map normal and injured brain tissue and compute appropriate quantitative measurements in support of a large number of investigations. We present a systematic methodology named FARSIGHT (fluorescence association rules for image-based insight), combining multi-parameter immuno-histochemical labeling, three-dimensional confocal microscopy, linear unmixing, segmentation, and associative morphometry to map and quantify diverse aspects of brain tissue. In addition to morphometric measurements of individual structures from individual imaging channels, we propose the use of distance maps, distance-weighted integrals, and distributions as an efficient basis for computing a versatile set of such associative measurements. These features enable classification of all the major cell types, and computation of their inter-relationships.

This work was supported by KOSEF through NBS-ERC and its international collaboration program between NBS and Cornell Bioengineering program.

This study was supported by the International Collaboration Program of NBSERC/ KOSEF and NIH/NIBIB, R01-EB- 000359.

This study was supported by the International Collaboration Program of NBS-ERC/KOSEF and NIH/NIBIB, R01- EB-000359.