Fig. 8.9 Batch processing result viewer

8.5 Selected Applications

8.5.1Neurite Tracing for Drug Discovery and Functional Genomics

High Content Screening or Analysis (HCS, respectively HCA) has virtually become an obligatory step of the Drug Development process. Cells in small transparent wells in a 96-, 384-, or 1,536-microplate format are exposed in a fully automated manner to thousands of different candidate compounds (see Table 8.2). They are then imaged and analysed using computer vision algorithms for evidence of drug action. In the case of neuronal cells, such evidence includes the growth of neuronal projections (neurites), but it can also include receptor trafficking, apoptosis, motility, as well as many other assays [16]. Measuring neurite dynamics is a particularly direct and informative approach but it is also challenging because neurites tend to be very thin, long, and may present extensive branching behaviour.

Some drugs trigger spectacular effects on neurites (e.g., nocodazole destabilizes microtubules thus inducing neurite retraction). More often however, dendritic arbours are altered in subtle ways only. Pharma is generally interested in changes to the length, shape and complexity of neurites. In fact, most of the general image features described in Sect. 8.2.5 are directly relevant for this particular application.

Fig. 8.10 (a) Original image showing astrocyte nuclei. (b) Nuclei identified by the software are gray coded, with surrogate cellular region boundaries overlaid in white. (c) Original image showing staining of GFAP fibres of the cytoskeleton. (d) Linear features identified by the software and gray coded as per nuclei, with surrogate cellular region boundaries overlaid in white

Generally, one does not know in advance how the phenotype will be altered. Therefore, it is desirable to apply as wide a spectrum of quantitative features as possible. Neuronal phenotype may also be altered by mutations, or by changes in the protein expression level elicited, for example, by small inhibitory RNAs. In collaboration with the Group of S.S. Tan and J.M. Gunnersen at the Howard Florey institute, we have been particularly interested in uncovering the role of the Seizure-related protein type 6 (Sez-6) [15]. From mouse behavioural studies, Sez-6 had already been implicated in cognitive processes but it was not clear yet whether the cell morphology was affected. In general, the biological variability across cells precludes drawing definitive conclusions from observing by eye a limited number of cells. Indeed, an individual knockout cell (lacking Sez-6) may appear more similar

to a wild type cell (possessing Sez-6) than to another knockout cell (Fig. 8.9). It is only when large numbers of cells are systematically analysed that statistically significant differences can be uncovered. In conducting these comparisons, it is extremely important to ensure that the analysis is performed identically on both the knockout and the wild-type images.

Our results demonstrated clearly that while the neurite field area was not affected, the mutation both increased branching and diminished the mean branch length. The full biological significance of these findings is not yet appreciated but these experiments clearly indicate that the geometry of neurite arbours is in large part under genetic control.

8.5.2 Using Linear Features to Quantify Astrocyte Morphology

In this example, we show how linear feature detection can be used to characterise morphological changes in the cytoskeleton of astrocytes, as induced by kinase inhibitors. This was part of a larger study into the role played by astrocytic glutamate transporters in maintaining brain homeostasis [17].

Fig. 8.11 The sensitivity of our linear feature proved helpful in this bacterial segmentation problem. By themselves, the detected edges (in gray) would not be sufficient to segment cells successfully. Together with the detected edges, our linear features (in white) form a double barrier system that enables accurate segmentation

It is often important to make measurements on a per-cell basis rather than on a per-image basis. To achieve this, one needs some way to identify the extent of each cell. This is done either directly by acquiring an additional image of a labelled cytoplasmic protein, or indirectly by generating a surrogate for the cell extent. The surrogate commonly used in cellular screening requires the capture of an additional image of labelled nuclei, the segmentation of those nuclei and the placing of a doughnut or ring around each nucleus. If cells are isolated, the surrogate cell region will appear roughly elliptical and the approximation to the actual cell shape tends to be crude. However, if cells are closely packed (as they often are in screening assays), the surrogate cell regions from neighboring nuclei deform to the midpoint between the two nuclei. This gives rise to regions which are close to the actual cell shapes (Fig. 8.11).

Within these surrogate cell regions, we quantify the features of the linear structures forming the astrocyte cytoskeleton (see Table 8.3). These measures have been used by our collaborators, O’Shea et al. of the Howard Florey Institute, to quantify the changes induced by the Rho-kinase inhibitor HA1077 in primary cultures of mouse astrocytes.

The astrocyte cytoskeleton was labelled using immunocytochemical staining for the astrocytic intermediate filament protein GFAP. Nuclei were labelled using Hoechst 33342. Figure 8.11 shows a sample nucleus and cytoskeleton image, along with the detected nuclei, the calculated surrogate cell extent and the detected lines in the cytoskeleton. Treatment with HA1077 (100 μM) produced rapid (<1h) and persistent changes in astrocytic morphology. The lineAngleVar feature (variance of the orientation angles of the “lines” within the cells) was significantly reduced by HA1077 (to 81 ± 4% of control, p < 0.05). A low lineAngleVar means that the