A systematic efficacy analysis of tuberculosis treatment with BPaL-containing regimens using a multiscale modeling approach.

Tuberculosis (TB) is a life-threatening infectious disease. The standard treatment is up to 90% effective; however, it requires the administration of four antibiotics (isoniazid, rifampicin, pyrazinamide, and ethambutol [HRZE]) over long time periods. This harsh treatment process causes adherence issues for patients because of the long treatment times and a myriad of adverse effects. Therefore, the World Health Organization has focused goals of shortening standard treatment regimens for TB in their End TB Strategy efforts, which aim to reduce TB-related deaths by 95% by 2035. For this purpose, many novel and promising combination antibiotics are being explored that have recently been discovered, such as the bedaquiline, pretomanid, and linezolid (BPaL) regimen. As a result, testing the number of possible combinations with all possible novel regimens is beyond the limit of experimental resources. In this study, we present a unique framework that uses a primate granuloma modeling approach to screen many combination regimens that are currently under clinical and experimental exploration and assesses their efficacies to inform future studies. We tested well-studied regimens such as HRZE and BPaL to evaluate the validity and accuracy of our framework. We also simulated additional promising combination regimens that have not been sufficiently studied clinically or experimentally, and we provide a pipeline for regimen ranking based on their efficacies in granulomas. Furthermore, we showed a correlation between simulation rankings and new marmoset data rankings, providing evidence for the credibility of our framework. This framework can be adapted to any TB regimen and can rank any number of single or combination regimens.

Development and Analysis of Multiscale Models for Tuberculosis: From Molecules to Populations.

Although infectious disease dynamics are often analyzed at the macro-scale, increasing numbers of drug-resistant infections highlight the importance of within-host modeling that simultaneously solves across multiple scales to effectively respond to epidemics. We review multiscale modeling approaches for complex, interconnected biological systems and discuss critical steps involved in building, analyzing, and applying such models within the discipline of model credibility. We also present our two tools: CaliPro, for calibrating multiscale models (MSMs) to datasets, and tunable resolution, for fine- and coarse-graining sub-models while retaining insights. We include as an example our work simulating infection with Mycobacterium tuberculosis to demonstrate modeling choices and how predictions are made to generate new insights and test interventions. We discuss some of the current challenges of incorporating novel datasets, rigorously training computational biologists, and increasing the reach of MSMs. We also offer several promising future research directions of incorporating within-host dynamics into applications ranging from combinatorial treatment to epidemic response.

Semi-automated colony-forming unit counting for biosafety level 3 laboratories.

Biosafety level 3 decontamination precautions motivate measuring microbial colonies using consumable photography instead of expensive automated plate counters or smartphones, and assaying drug treatments-with multiple concentrations per treatment, replicates, and controls-produces hundreds of images. Here, we present a protocol for semi-automated image analysis by hand-tuning three parameters. The parameters control for non-uniform colony growth and artifacts such as lid condensation, reflections, and plating streaks. We describe steps to prepare images, tune parameters, and plot dose-response relationships. For complete details on the use and execution of this protocol, please refer to Larkins-Ford et al.1.

Calibration methods to fit parameters within complex biological models.

Mathematical and computational models of biological systems are increasingly complex, typically comprised of hybrid multi-scale methods such as ordinary differential equations, partial differential equations, agent-based and rule-based models, etc. These mechanistic models concurrently simulate detail at resolutions of whole host, multi-organ, organ, tissue, cellular, molecular, and genomic dynamics. Lacking analytical and numerical methods, solving complex biological models requires iterative parameter sampling-based approaches to establish appropriate ranges of model parameters that capture corresponding experimental datasets. However, these models typically comprise large numbers of parameters and therefore large degrees of freedom. Thus, fitting these models to multiple experimental datasets over time and space presents significant challenges. In this work we undertake the task of reviewing, testing, and advancing calibration practices across models and dataset types to compare methodologies for model calibration. Evaluating the process of calibrating models includes weighing strengths and applicability of each approach as well as standardizing calibration methods. Our work compares the performance of our model agnostic Calibration Protocol (CaliPro) with approximate Bayesian computing (ABC) to highlight strengths, weaknesses, synergies, and differences among these methods. We also present next-generation updates to CaliPro. We explore several model implementations and suggest a decision tree for selecting calibration approaches to match dataset types and modeling constraints.

Optimization of sampling pattern and the design of Fourier ptychographic illuminator.

Fourier ptychography (FP) is a recently developed imaging approach that facilitates high-resolution imaging beyond the cutoff frequency of the employed optics. In the original FP approach, a periodic LED array is used for sample illumination, and therefore, the scanning pattern is a uniform grid in the Fourier space. Such a uniform sampling scheme leads to 3 major problems for FP, namely: 1) it requires a large number of raw images, 2) it introduces the raster grid artefacts in the reconstruction process, and 3) it requires a high-dynamic-range detector. Here, we investigate scanning sequences and sampling patterns to optimize the FP approach. For most biological samples, signal energy is concentrated at low-frequency region, and as such, we can perform non-uniform Fourier sampling in FP by considering the signal structure. In contrast, conventional ptychography perform uniform sampling over the entire real space. To implement the non-uniform Fourier sampling scheme in FP, we have designed and built an illuminator using LEDs mounted on a 3D-printed plastic case. The advantages of this illuminator are threefold in that: 1) it reduces the number of image acquisitions by at least 50% (68 raw images versus 137 in the original FP setup), 2) it departs from the translational symmetry of sampling to solve the raster grid artifact problem, and 3) it reduces the dynamic range of the captured images 6 fold. The results reported in this paper significantly shortened acquisition time and improved quality of FP reconstructions. It may provide new insights for developing Fourier ptychographic imaging platforms and find important applications in digital pathology.

Microscopy illumination engineering using a low-cost liquid crystal display.

Illumination engineering is critical for obtaining high-resolution, high-quality images in microscope settings. In a typical microscope, the condenser lens provides sample illumination that is uniform and free from glare. The associated condenser diaphragm can be manually adjusted to obtain the optimal illumination numerical aperture. In this paper, we report a programmable condenser lens for active illumination control. In our prototype setup, we used a $15 liquid crystal display as a transparent spatial light modulator and placed it at the back focal plane of the condenser lens. By setting different binary patterns on the display, we can actively control the illumination and the spatial coherence of the microscope platform. We demonstrated the use of such a simple scheme for multimodal imaging, including bright-field microscopy, darkfield microscopy, phase-contrast microscopy, polarization microscopy, 3D tomographic imaging, and super-resolution Fourier ptychographic imaging. The reported illumination engineering scheme is cost-effective and compatible with most existing platforms. It enables a turnkey solution with high flexibility for researchers in various communities. From the engineering point-of-view, the reported illumination scheme may also provide new insights for the development of multimodal microscopy and Fourier ptychographic imaging.

FPscope: a field-portable high-resolution microscope using a cellphone lens.

The large consumer market has made cellphone lens modules available at low-cost and in high-quality. In a conventional cellphone camera, the lens module is used to demagnify the scene onto the image plane of the camera, where image sensor is located. In this work, we report a 3D-printed high-resolution Fourier ptychographic microscope, termed FPscope, which uses a cellphone lens in a reverse manner. In our platform, we replace the image sensor with sample specimens, and use the cellphone lens to project the magnified image to the detector. To supersede the diffraction limit of the lens module, we use an LED array to illuminate the sample from different incident angles and synthesize the acquired images using the Fourier ptychographic algorithm. As a demonstration, we use the reported platform to acquire high-resolution images of resolution target and biological specimens, with a maximum synthetic numerical aperture (NA) of 0.5. We also show that, the depth-of-focus of the reported platform is about 0.1 mm, orders of magnitude longer than that of a conventional microscope objective with a similar NA. The reported platform may enable healthcare accesses in low-resource settings. It can also be used to demonstrate the concept of computational optics for educational purposes.

High-resolution fluorescence imaging via pattern-illuminated Fourier ptychography.

Fluorescence microscopy plays a vital role in modern biological research and clinical diagnosis. Here, we report an imaging approach, termed pattern-illuminated Fourier ptychography (FP), for fluorescence imaging beyond the diffraction limit of the employed optics. This approach iteratively recovers a high-resolution fluorescence image from many pattern-illuminated low-resolution intensity measurements. The recovery process starts with one low-resolution measurement as the initial guess. This initial guess is then sequentially updated by other measurements, both in the spatial and Fourier domains. In the spatial domain, we use the pattern-illuminated low-resolution images as intensity constraints for the sample estimate. In the Fourier domain, we use the incoherent optical-transfer-function of the objective lens as the object support constraint for the solution. The sequential updating process is then repeated until the sample estimate converges, typically for 5-20 times. Different from the conventional structured illumination microscopy, any unknown pattern can be used for sample illumination in the reported framework. In particular, we are able to recover both the high-resolution sample image and the unknown illumination pattern at the same time. As a demonstration, we improved the resolution of a conventional fluorescence microscope beyond the diffraction limit of the employed optics. The reported approach may provide an alternative solution for structure illumination microscopy and find applications in wide-field, high-resolution fluorescence imaging.

Spectral multiplexing and coherent-state decomposition in Fourier ptychographic imaging.

Information multiplexing is important for biomedical imaging and chemical sensing. In this paper, we report a microscopy imaging technique, termed state-multiplexed Fourier ptychography (FP), for information multiplexing and coherent-state decomposition. Similar to a typical Fourier ptychographic setting, we use an array of light sources to illuminate the sample from different incident angles and acquire corresponding low-resolution images using a monochromatic camera. In the reported technique, however, multiple light sources are lit up simultaneously for information multiplexing, and the acquired images thus represent incoherent summations of the sample transmission profiles corresponding to different coherent states. We show that, by using the state-multiplexed FP recovery routine, we can decompose the incoherent mixture of the FP acquisitions to recover a high-resolution sample image. We also show that, color-multiplexed imaging can be performed by simultaneously turning on R/G/B LEDs for data acquisition. The reported technique may provide a solution for handling the partially coherent effect of light sources used in Fourier ptychographic imaging platforms. It can also be used to replace spectral filter, gratings or other optical components for spectral multiplexing and demultiplexing. With the availability of cost-effective broadband LEDs, the reported technique may open up exciting opportunities for computational multispectral imaging.