Cancer detection from stained biopsies using high-speed spectral imaging.

The escalating demand for diagnosing pathological biopsies requires the procedures to be expedited and automated. The existing imaging systems for measuring biopsies only measure color, and even though a lot of effort is invested in deep learning analysis, there are still serious challenges regarding the performance and validity of the data for the intended medical setting. We developed a system that rapidly acquires spectral images from biopsies, followed by spectral classification algorithms. The spectral information is remarkably more informative than the color information, and leads to very high accuracy in identifying cancer cells, as tested on tens of cancer cases. This was improved even more by using artificial intelligence algorithms that required a rather small training set, indicating the high level of information that exists in the spectral images. The most important spectral differences are observed in the nucleus and they are related to aneuploidy in tumor cells. Rapid spectral imaging measurement therefore can bridge the gap in the machine-aided diagnostics of whole biopsies, thus improving patient care, and expediting the treatment procedure.

Rapid microscopy measurement of very large spectral images.

The spectral content of a sample provides important information that cannot be detected by the human eye or by using an ordinary RGB camera. The spectrum is typically a fingerprint of the chemical compound, its environmental conditions, phase and geometry. Thus measuring the spectrum at each point of a sample is important for a large range of applications from art preservation through forensics to pathological analysis of a tissue section. To date, however, there is no system that can measure the spectral image of a large sample in a reasonable time. Here we present a novel method for scanning very large spectral images of microscopy samples even if they cannot be viewed in a single field of view of the camera. The system is based on capturing information while the sample is being scanned continuously 'on the fly'. Spectral separation implements Fourier spectroscopy by using an interferometer mounted along the optical axis. High spectral resolution of ~5 nm at 500 nm could be achieved with a diffraction-limited spatial resolution. The acquisition time is fairly high and takes 6-8 minutes for a sample size of 10mm x 10mm measured under a bright-field microscope using a 20X magnification.

Exploring chromatin organization mechanisms through its dynamic properties.

The organization of the genome in the nucleus is believed to be crucial for different cellular functions. It is known that chromosomes fold into distinct territories, but little is known about the mechanisms that maintain these territories. To explore these mechanisms, we used various live-cell imaging methods, including single particle tracking to characterize the diffusion properties of different genomic regions in live cells. Chromatin diffusion is found to be slow and anomalous; in vast contrast, depletion of lamin A protein significantly increases chromatin motion, and the diffusion pattern of chromatin transforms from slow anomalous to fast normal. More than this, depletion of lamin A protein also affects the dynamics of nuclear bodies. Our findings indicate that chromatin motion is mediated by lamin A and we suggest that constrained chromatin mobility allows to maintain chromosome territories. Thus, the discovery of this function of nucleoplasmic lamin A proteins sheds light on the maintenance mechanism of chromosome territories in the interphase nucleus, which ensures the proper function of the genome.

Automatic classification of cancer cells in multispectral microscopic images of lymph node samples.

Histopathological analysis is crucial for the diagnosis of a large number of cancer types. A lot of progress has been made in the development of molecular based assays, but many of the cases still require the careful analysis of the stained tissue under a bright-field microscope and its analysis. This procedure is costly and time-consuming. We present a novel method for classification of cancer cells in lymph node images. It is based on the measurement of the spectral image of hematoxylin and eosin stained sample under the microscope and the analysis of the acquired data using state of the art machine learning techniques. The method is based on the analysis of the spectral information of the cells as well as their morphological properties. A large number of descriptors is extracted for each cell location, which are used to train a supervised classifier which discriminates between normal and cancer cells. We show that a reliable analysis can be made with detection rate (recall) of 81%-100% for the cancer class.

Mating regulates neuromodulator ensembles at nerve termini innervating the Drosophila reproductive tract.

Upon mating, regions of the female reproductive tract mature and alter their function [1-3], for example to facilitate storage of sperm or control the release of eggs [4-6]. The female's nervous system and neuromodulators play important roles in her responses to mating [7-13]. However, it is difficult to reconcile the reproductive tract's many changing but coordinated events with the small set of neuromodulators present [14-18]. We hypothesized that each part of the reproductive tract contains a characteristic combination of neuromodulators that confer unique identities on each region and that postmating changes in these combinations coordinate subsequent actions. We examined the presence, locations, and levels of neuromodulators and related molecules ("signaling molecules") in the reproductive tract of Drosophila melanogaster females before and after mating: the biogenic amine octopamine, which regulates ovulation rate in Drosophila and locusts [7, 14-20]; serotonin, which regulates muscle contraction in locust oviducts [21]; and the FMRF amide dromyosuppressin, which regulates contraction of Drosophila heart muscle [22] and may regulate muscle contractions in the reproductive tract, if it is expressed there. We find that separate aspects of mating (sperm, seminal proteins, and physical effects) independently modulate the release of signaling molecules. Each reproductive tract subregion displays a characteristic combination of signaling molecule release, resulting in a unique functional identity. These patterns, and thus functions, change reproducibly after mating. Thus, one event (mating) promotes new combinations of signaling molecules that endow different parts of the reproductive tract with unique temporal and spatial identities that facilitate many aspects of fertilization.

Dynamic analysis of a diffusing particle in a trapping potential.

The dynamics of a diffusing particle in a potential field is ubiquitous in physics, and it plays a pivotal role in single-molecule studies. We present a formalism for analyzing the dynamics of diffusing particles in harmonic potentials at low Reynolds numbers using the time evolution of the particle probability distribution function. We demonstrate the power of the formalism by simulation and by measuring and analyzing a nanobead tethered to a single DNA molecule. It allows one to simultaneously extract all the parameters that describe the system, namely, the diffusion coefficient and the restoring-force constant.

Force-free measurements of the conformations of DNA molecules tethered to a wall.

Using an optimized combination of tethered particle motion method, total internal reflection, and a gold nanobead, we measured the three-dimensional distribution of the free end of a tethered DNA molecule. The distribution along the axial z direction (perpendicular to the surface) is found to be Rayleigh-like, in agreement with wormlike chain and freely jointed chain simulations. Using these simulations, we show that the presence of the wall increases the correlations between the orientations of neighboring chain segments compared to free DNA. While the measured and the simulated planar (xy) distributions always agree with that of a Gaussian-random-walk (GRW) model, for short DNA lengths (1 µm) studied in our experiment, the corresponding axial (z) distributions deviate from those predicted for a GRW confined to half-space.

HU protein induces incoherent DNA persistence length.

HU is a highly conserved protein that is believed to play an important role in the architecture and dynamic compaction of bacterial DNA. Its ability to control DNA bending is crucial for functions such as transcription and replication. The effects of HU on the DNA structure have been studied so far mainly by single molecule methods that require us to apply stretching forces on the DNA and therefore may perturb the DNA-protein interaction. To overcome this hurdle, we study the effect of HU on the DNA structure without applying external forces by using an improved tethered particle motion method. By combining the results with DNA curvature analysis from atomic force microscopy measurements we find that the DNA consists of two different curvature distributions and the measured persistence length is determined by their interplay. As a result, the effective persistence length adopts a bimodal property that depends primarily on the HU concentration. The results can be explained according to a recently suggested model that distinguishes single protein binding from cooperative protein binding.