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The emergence of labeling strategies and live cell imaging methods enables the imaging of chromatin in living cells at single digit nanometer resolution as well as milliseconds temporal resolution. These technical breakthroughs revolutionize our understanding of chromatin structure, dynamics and functions. Single molecule tracking algorithms are usually preferred to quantify the movement of these intranucleus elements to interpret the spatiotemporal evolution of the chromatin. In this review, we will first summarize the fluorescent labeling strategy of chromatin in live cells which will be followed by a systematic comparison of live cell imaging instrumentation. With the proper microscope, we will discuss the image analysis pipelines to extract the biophysical properties of the chromatin. Finally, we expect to give practical suggestions to broad biologists on how to select methods and link to the model properly according to different investigation purposes.

Image-based particle tracking is an essential tool to answer research questions in cell biology and beyond. A major challenge of particle tracking in living systems is that low light exposure is required to avoid phototoxicity and photobleaching. In addition, high-speed imaging used to fully capture particle motion dictates fast image acquisition rates. Short exposure times come at the expense of tracking accuracy. This is generally true for quantitative microscopy approaches and particularly relevant to single molecule tracking where the number of photons emitted from a single chromophore is limited. Image restoration methods based on deep learning dramatically improve the signal-to-noise ratio in low-exposure datasets. However, it is not clear whether images generated by these methods yield accurate quantitative measurements such as diffusion parameters in (single) particle tracking experiments. Here, we evaluate the performance of two popular deep learning denoising software packages for particle tracking, using synthetic datasets and movies of diffusing chromatin as biological examples. With synthetic data, both supervised and unsupervised deep learning restored particle motions with high accuracy in two-dimensional datasets, whereas artifacts were introduced by the denoisers in 3D datasets. Experimentally, we found that, while both supervised and unsupervised approaches improved the number of trackable particles and tracking accuracy, supervised learning generally outperformed the unsupervised approach, as expected. We also highlight that with extremely noisy image sequences, deep learning algorithms produce deceiving artifacts, which underscores the need to carefully evaluate the results. Finally, we address the challenge of selecting hyper-parameters to train convolutional neural networks by implementing a frugal Bayesian optimizer that rapidly explores multidimensional parameter spaces, identifying networks yielding optional particle tracking accuracy. Our study provides quantitative outcome measures of image restoration using deep learning. We anticipate broad application of the approaches presented here to critically evaluate artificial intelligence solutions for quantitative microscopy.

Chronic Kidney Disease of unknown aetiology (CKDu) is a prevalent disease in the North Central Province of Sri Lanka. Towards the latter stages of the disease, kidney function fails by 80%. During the initial stages of CKDu, interstitial fibrosis is formed and grows as the disease progresses. The cause of the disease remains elusive and early detection is vital to arrest the progressive decline of kidney function. The objective of this study is to construct a computer program to perform texture analysis on ultrasound kidney images and extract various features that can be used to distinguish between normal and diseased kidney patients. The computer program was developed using MATLAB and a user interface was created to perform mathematical operations such as: Fourier analysis to extract Root Mean Square and First Moment values and Grey Level Co-occurrence Matrix (GLCM) to extract Homogeneity and Sum Average values. A sample of ultrasound images were taken from 32 patients. Region of interest (ROI) selection was performed on entire kidney, cortex region and white (renal medulla or renal sinus) region separately. Among these methods Root Mean Square values over the entire kidney (p=0.03) and cortex region (p=0.0049) gave significant results in distinguishing between normal and diseased kidneys.