A novel Streptococcus pneumoniae human challenge model demonstrates Treg lymphocyte recruitment to the infection site.

To investigate local tissue responses to infection we have developed a human model of killed Streptococcus pneumoniae challenge by intradermal injection into the forearm. S. pneumoniae intradermal challenge caused an initial local influx of granulocytes and increases in TNF, IL6 and CXCL8. However, by 48 h lymphocytes were the dominant cell population, mainly consisting of CD4 and CD8 T cells. Increases in local levels of IL17 and IL22 and the high proportion of CD4 cells that were CCR6+ suggested a significant Th17 response. Furthermore, at 48 h the CD4 population contained a surprisingly high proportion of likely memory Treg cells (CCR6 positive and CD45RA negative CD4+CD25highCD127low cells) at 39%. These results demonstrate that the intradermal challenge model can provide novel insights into the human response to S. pneumoniae and that Tregs form a substantial contribution of the normal human lymphocyte response to infection with this important pathogen.

Robust deep learning optical autofocus system applied to automated multiwell plate single molecule localization microscopy.

We presenta robust, long-range optical autofocus system for microscopy utilizing machine learning. This can be useful for experiments with long image data acquisition times that may be impacted by defocusing resulting from drift of components, for example due to changes in temperature or mechanical drift. It is also useful for automated slide scanning or multiwell plate imaging where the sample(s) to be imaged may not be in the same horizontal plane throughout the image data acquisition. To address the impact of (thermal or mechanical) fluctuations over time in the optical autofocus system itself, we utilize a convolutional neural network (CNN) that is trained over multiple days to account for such fluctuations. To address the trade-off between axial precision and range of the autofocus, we implement orthogonal optical readouts with separate CNN training data, thereby achieving an accuracy well within the 600 nm depth of field of our 1.3 numerical aperture objective lens over a defocus range of up to approximately +/-100 µm. We characterize the performance of this autofocus system and demonstrate its application to automated multiwell plate single molecule localization microscopy.

Many microscopy experiments involve extended imaging of samples over timescales from minutes to days, during which the microscope can 'drift' out of focus. When imaging at high magnification, the depth of field is of the order of one micron and so the imaging system should keep the sample in the focal plane of the microscope objective lens to this precision. Unfortunately, temperature changes in the laboratory can cause thermal expansion of microscope components that can move the focal plane by more than a micron and such changes can occur on a timescale of minutes. This is a particular issue for super-resolved microscopy experiments using single molecule localization microscopy (SMLM) techniques, for which 1000s of images are acquired, and for automated imaging of multiple samples in multiwell plates. It is possible to maintain the sample in the focal plane focus position by either automatically moving the sample or adjusting the imaging system, for example by moving the objective lens. This is called 'autofocus' and is frequently achieved by reflecting a light beam from the microscope coverslip and measuring its position of beam profile as a function of defocus of the microscope. The correcting adjustment is then usually calculated analytically but there is recent interest in using machine learning techniques to determine the required focussing adjustment. Here, we present a system that uses a neural network to determine the required defocus correcting adjustment from camera images of a laser beam that is reflected from the coverslip. Unfortunately, this approach will only work when the microscope is in the same condition as it was when the neural network was trained - and this can be compromised by the same drift of the optical system that causes the defocus needing to be corrected. We show, however, that by training a neural network over an extended period, for example 10 days, this approach can 'learn' about the optical system drifts and provide the required autofocus function. We also show that an optical system utilizing a rectangular slit can make two measurements of the defocus simultaneously, with one measurement being optimized for high accuracy over a limited range (±10 µm) near focus and the other providing lower accuracy but over a much longer range (±100 µm). This robust autofocus system is suitable for automated super-resolved microscopy of arrays of samples in a multiwell plate using SMLM, for which an experiment routinely lasts more than 5 h.

Monocyte Adhesion, Migration, and Extracellular Matrix Breakdown Are Regulated by Integrin αVß3 in Mycobacterium tuberculosis Infection.

In tuberculosis (TB), the innate inflammatory immune response drives tissue destruction, morbidity, and mortality. Monocytes secrete matrix metalloproteinases (MMPs), which have key roles in local tissue destruction and cavitation. We hypothesized that integrin signaling might regulate monocyte MMP secretion in pulmonary TB during cell adhesion to the extracellular matrix (ECM). Adhesion to type I collagen and fibronectin by Mycobacterium tuberculosis-stimulated monocytes increased MMP-1 gene expression by 2.6-fold and 4.3-fold respectively, and secretion by 60% (from 1208.1 ± 186 to 1934.4 ± 135 pg/ml; p < 0.0001) and 63% (1970.3 ± 95 pg/ml; p < 0.001). MMP-10 secretion increased by 90% with binding to type I collagen and 55% with fibronectin, whereas MMP-7 increased 57% with collagen. The ECM did not affect the secretion of tissue inhibitors of metalloproteinases-1 or -2. Integrin αVß3 surface expression was specifically upregulated in stimulated monocytes and was further increased after adhesion to type I collagen. Binding of either ß3 or αV integrin subunits increased MMP-1/10 secretion in M. tuberculosis-stimulated monocytes. In a cohort of TB patients, significantly increased integrin ß3 mRNA accumulation in induced sputum was detected, to our knowledge, for the first time, compared with control subjects (p < 0.05). Integrin αVß3 colocalized with areas of increased and functionally active MMP-1 on infected monocytes, and αVß3 blockade markedly decreased type I collagen breakdown, and impaired both monocyte adhesion and leukocyte migration in a transwell system (p < 0.0001). In summary, our data demonstrate that M. tuberculosis stimulation upregulates integrin αVß3 expression on monocytes, which upregulates secretion of MMP-1 and -10 on adhesion to the ECM. This leads to increased monocyte recruitment and collagenase activity, which will drive inflammatory tissue damage.