An Interpretable Classification Model Using Gluten-Specific TCR Sequences Shows Diagnostic Potential in Coeliac Disease.

Coeliac disease (CeD) is a T-cell mediated enteropathy triggered by dietary gluten which remains substantially under-diagnosed around the world. The diagnostic gold-standard requires histological assessment of intestinal biopsies taken at endoscopy while consuming a gluten-containing diet. However, there is a lack of concordance between pathologists in histological assessment, and both endoscopy and gluten challenge are burdensome and unpleasant for patients. Identification of gluten-specific T-cell receptors (TCRs) in the TCR repertoire could provide a less subjective diagnostic test, and potentially remove the need to consume gluten. We review published gluten-specific TCR sequences, and develop an interpretable machine learning model to investigate their diagnostic potential. To investigate this, we sequenced the TCR repertoires of mucosal CD4+ T cells from 20 patients with and without CeD. These data were used as a training dataset to develop the model, then an independently published dataset of 20 patients was used as the testing dataset. We determined that this model has a training accuracy of 100% and testing accuracy of 80% for the diagnosis of CeD, including in patients on a gluten-free diet (GFD). We identified 20 CD4+ TCR sequences with the highest diagnostic potential for CeD. The sequences identified here have the potential to provide an objective diagnostic test for CeD, which does not require the consumption of gluten.

A novel abrasive water jet machining technique for rapid fabrication of three-dimensional microfluidic components.

Microfluidic lab-on-a-chip devices are usually fabricated using replica molding, with poly(dimethylsiloxane) (PDMS) casting on a mold. Most common techniques used to fabricate microfluidic molds, such as photolithography and soft lithography, require costly facilities such as a cleanroom, and complicated steps, especially for the fabrication of three-dimensional (3D) features. For example, an often-desired 3D microchannel feature consists of intersecting channels with depth variations. This type of 3D flow focusing geometry has applications in flow cytometry and droplet generation. Various manufacturing techniques have recently been developed for the rapid fabrication of such 3D microfluidic features. In this paper, we describe a new method of mold fabrication that utilizes water jet cutting technology to fabricate free-standing structures on mild steel sheets to make a mold for PDMS casting. As a proof-of-concept, we use this fabrication technique to make a PDMS chip that has a 3D flow focusing junction, an inlet for the sample fluid, two inlets for the sheath fluid, and an outlet. The flow focusing junction is patterned into the PDMS slab with an abrupt, nearly stepwise change to the depth of the microchannel junction. We use confocal microscopy to visualize the 3D flow focusing of a sample flow using this geometry, and we also use the same geometry to generate water-in-oil droplets. This alternative approach to create microfluidic molds is versatile and may find utility in reducing the cost and complexity involved in fabricating 3D features in microfluidic devices.