Single-shot super-resolution total internal reflection fluorescence microscopy

Guo, M.; Chandris, P.; Giannini, J. P.; Trexler, A. J.; Fischer, R.; Chen, J.; Vishwasrao, H. D.; Rey-Suarez, I.; Wu, Y.; Waterman, C. M.; Patterson, G. H.; Upadhyaya, A.; Taraska, J.; Shroff, H.

doi:10.1101/182121

bioRxiv · 10.1101/182121

Single-shot super-resolution total internal reflection fluorescence microscopy

Guo, M.·Chandris, P.·Giannini, J. P.·Trexler, A. J.·Fischer, R.·Chen, J.·Vishwasrao, H. D.·Rey-Suarez, I.·Wu, Y.·Waterman, C. M.·Patterson, G. H.·Upadhyaya, A.·Taraska, J.·Shroff, H.

View original source ↗Export metadata ↓

Abstract

We demonstrate a simple method for combining instant structured illumination microscopy (SIM) with total internal reflection fluorescence microscopy (TIRF), doubling the spatial resolution of TIRF (down to 115 +/-13 nm) and enabling imaging frame rates up to 100 Hz over hundreds of time points. We apply instant TIRF-SIM to multiple live samples, achieving rapid, high contrast super-resolution imaging in close proximity to the coverslip surface.

Source connections

Published article ↗

Explore related subjects

bioengineering Preprint

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Guo, M., Chandris, P., Giannini, J. P., Trexler, A. J., Fischer, R., Chen, J., Vishwasrao, H. D., Rey-Suarez, I., Wu, Y., Waterman, C. M., Patterson, G. H., Upadhyaya, A., Taraska, J., Shroff, H.. 2017-08-29. Single-shot super-resolution total internal reflection fluorescence microscopy. https://doi.org/10.1101/182121

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

Impaired loading following denervation elicits dynamic, differential growth trajectories of musculoskeletal tissues

Physical forces dictate the function and maintenance of adult musculoskeletal tissues. Yet, the impact of biophysical cues on the growth and maturation of these tissues, especially dense connective tissues, remains unclear. Here, we investigated the effects of neonatal mechanical loading on early postnatal growth of the musculoskeletal tissues of the lower limb, with particular focus on the Achilles tendon. We performed sciatic nerve resection (SNR) on one-day-old mice to denervate the hamstrings and distal hindlimb muscles. Sciatic Nerve Resection resulted in severe and sustained alterations in gait, accompanied by an altered transcriptional landscape in distal musculoskeletal tissues, with marked reductions in bone and tendon growth and smaller, fibrotic hindlimb musculature. Sciatic nerve resection impaired cumulative growth in the Achilles tendon, shifting cell proliferation and matrix-driven growth trajectories and altering mechanotransduction gene programs toward inflammation and matrix catabolism with age. This work highlights the essential role of mechanical loading in driving mechanosignaling and postnatal growth trajectories in musculoskeletal tissues of the developing limb, with implications for overall musculoskeletal health in adulthood.

bioengineering↗

bioRxiv2026

MiniMAP: An automatic pipeline for paediatric lower limb musculoskeletal model generation

The generation of paediatric musculoskeletal models often requires compromise between data availability, accuracy, and computation time. Statistical shape models allow for rapid generation of bone geometries for use in musculoskeletal modelling. An articulated shape model allows for movement at the joints during optimisation to better fit to experimental data. Here we present a methodology for generating custom paediatric OpenSim models using an articulated shape model of the lower limb generated from CT scans of over 300 children aged 4 to 18 years old. Bone geometries are predicted using demographic factors (age, height, mass, and sex), linear bone measurements (bone widths and lengths), and 12 skin surface landmarks (L/R ASIS, L/R PSIS, medial/lateral epicondyles, and medial/lateral malleoli). To account for skin padding (bone to skin surface distance) a random forest algorithm was developed for skin padding prediction for these 12 landmarks. Accuracy of the model prediction was assessed using a leave-one-out analysis to assess landmark errors, bone surface errors, and clinical bone measurement errors (linear measurements and angular values). Motion capture and MRI data were collected from two paediatric test cases to test the workflow on unseen data. Skin padding values varied widely in this population with average values from 3.4mm at the lateral malleolus to 25.9mm at the PSIS. Skin padding was best predicted using age, height, mass, ASIS width, epicondylar width, and malleolar width with an average RMSE of 4.68mm. The articulated shape model predicted accurate bone geometries from skin surface landmarks with average bone surface RMSEs of 2.64 {+/-} 0.99mm, 1.91 {+/-} 0.59mm, and 1.69 {+/-} 0.56mm in the pelvis, femora, and tibiae/fibulae respectively. In the two unseen test cases, average bone surface errors ranged from 1.64 {+/-} 1.2mm to 3.41 {+/-} 3.1mm. This study demonstrated the accuracy of "MiniMAP", for the rapid generation of custom paediatric musculoskeletal models.

bioengineering↗

bioRxiv2026

A single-plane electrical impedance tomography framework for integrated liver and kidney assessment

Chronic liver and kidney diseases frequently coexist, but their assessment typically requires separate clinical workflows involving serum-based estimated glomerular filtration rate (eGFR) testing and liver elastography-based controlled attenuation parameter (CAP). Electrical impedance tomography (EIT) has recently shown promise for providing portable, non-invasive proxies for both reference measurements. However, existing multi-organ EIT assessment still requires different acquisition protocols for each organ. To simplify this procedure, we assessed the feasibility of a single-plane EIT framework for reconstructing organ-specific images and estimating liver and kidney health from one abdominal belt placement. Across diverse abdominal belt locations, opposite-plane deviation was significantly greater for eGFREIT estimates (2.92%; IQR, 0.46-12.53%) than for CAPEIT estimates (0.94%; IQR, 0.66-1.70%) (p < 0.01). For both organs, compared to images acquired at the conventional organ-specific planes, image correlation significantly decreased (both, p < 0.05) and normalized root mean squared error significantly increased (both, p < 0.05) at the furthest imaging plane, with these differences being numerically larger for the kidney. Together, these results supported the kidney plane as the preferred location for multi-organ assessment. Test-retest evaluation after belt removal and reapplication demonstrated that the correlation between two repeated CAPEIT estimates was just as strong at the kidney plane (R2 = 0.98, p < 0.001) as it was at the conventional liver plane (R2 = 0.97, p < 0.001). Finally, in an independent clinical cohort, kidney-plane CAPEIT estimates were significantly higher in participants with a high metabolic liver-risk profile (i.e. diabetes and high blood pressure) (p < 0.005). CAPEIT estimates also distinguished high and low metabolic risk groups with an AUC of 0.756. Together, these findings support the feasibility of a portable multi-organ EIT imaging framework capable of extracting complementary liver and kidney information from a single belt placement.

bioengineering↗