Manuscripts

Predicting targeted-and immunotherapeutic response outcomes in melanoma with single- cell Raman Spectroscopy and AI

Kai Chang, Mamatha Serasanambati, Baba Ogunlade, Hsiu-Ju Hsu, James Agolia, Ariel Stiber, Jeffrey Gu, Saurabh Sharma, Jay Chadokiya, Amanda Gonçalves, Fareeha Safir, Nhat Vu, Daniel Delitto, Amanda Kirane, Jennifer Dionne. “Predicting targeted-and immunotherapeutic response outcomes in melanoma with single- cell Raman Spectroscopy and AI.” JCO Precision Oncology. 2025. Preprint: bioRxiv 2025.05.16.654612.

Abstract: Identifying predictive biomarkers of immunotherapeutic response in melanoma remains an outstanding challenge. Existing transcriptomic and proteomic profiling methods of the tumor-immune microenvironment are costly and may not faithfully capture modifications actively impacting tumor behavior. Here, we present a non-destructive, single-cell approach combining Raman spectroscopy and machine learning (ML) that enables rapid cell profiling and therapeutic response prediction. We tested mouse and human melanoma cell lines alongside nine melanoma patient-derived samples. Each sample had known resistance profiles to a panel of targeted and immunotherapeutic inhibitors, including bemcentinib, cabozantinib, dabrafenib, nivolumab, and a combination of nivolumab and relatlimab. In cell lines, our single-cell Raman and ML approach achieved >96% differentiation accuracy across tumor microenvironment cell types and functional phenotypes. Formations of subclusters for persistent (e.g. drug-resistant) cells were observed based on genetic mutations rather than sample origin, with Raman signatures reflecting biochemical changes relevant to various therapeutic pathways. For patientsamples, we constructed a two-stage evaluation workflow to assess clinical drug resistance. Using therapy-specific random forests, our workflow correctly inferred resistance likelihoods for 30 of 33 clinically relevant patient-drug combinations (91% accuracy) unseen by our model with optimized labeling thresholds. Our scalable, prognostic model using single-cell Raman offers potential to advance clinical, multi-omic biomarker efforts and impact first- and second-line therapy selection assessments for precision medicine.

Upconverting microgauges reveal intraluminal force dynamics in vivo

Jason Casar, Claire McLellan, Cindy Shi, Ariel Stiber, Alice Lay, Chris Siefe, Abhinav Parakh, Malaya Gaerlan, X. Wendy Gu, Miriam B. Goodman , Jennifer A. Dionne. “Upconverting microgauges reveal intraluminal force dynamics in vivo.”, Nature, 637(8044), 76-83 (2025).

Abstract: The forces generated by action potentials in muscle cells shuttle blood, food, and waste products throughout the body’s luminal structures. While non-invasive electrophysiological techniques exist, most mechanosensitive tools cannot access luminal structures non-invasively. Here, we create non-toxic, ingestible mechanosensors to enable the quantitative study of luminal forces and apply them to study feeding in living Caenorhabditis elegans roundworms. These optical “microgauges” comprise upconverting NaY0.8Yb0.18Er0.02F4@NaYF4 nanoparticles (UCNPs) embedded in polystyrene microspheres. Combining optical microscopy and atomic force microscopy to study microgauges in vitro, we show that force evokes a linear and hysteresis-free change in the ratio of emitted red to green light. With fluorescence imaging and non-invasive electrophysiology, we show that adult C. elegans generate bite forces during feeding on the order of 10 µN and that the temporal pattern of force generation is aligned with muscle activity in the feeding organ. Moreover, the bite force we measure corresponds to Hertzian contact stresses within the pressure range used to lyse the worm’s bacterial food. Microgauges have the potential to enable quantitative studies that investigate how neuromuscular stresses are affected by aging, genetic mutations, and drug treatments in this and other luminal organs.

Very-large-scale integrated high quality factor nanoantenna pixels

Varun Dolia, Halleh Balch, Sahil Dagli, Sajjad Abdollahramezani, Hamish Carr Delgado, Parivash
Moradifar, Kai Chang, Ariel Stiber, Fareeha Safir, Mark Lawrence, Jack Hu, Jennifer A Dionne. “Very-large-scale- integrated high quality factor nanoantenna pixels.” Nature Nanotechnology, 19(9), 1290-1298 (2024).

Abstract: Metasurfaces precisely control the amplitude, polarization and phase of light, with applications spanning imaging, sensing, modulation and computing. Three crucial performance metrics of metasurfaces and their constituent resonators are the quality factor (Q factor), mode volume (Vm) and ability to control far-field radiation. Often, resonators face a trade-off between these parameters: a reduction in Vm leads to an equivalent reduction in Q, albeit with more control over radiation. Here we demonstrate that this perceived compromise is not inevitable: high quality factor, subwavelength Vm and controlled dipole-like radiation can be achieved simultaneously. We design high quality factor, very-large-scale-integrated silicon nanoantenna pixels (VINPix) that combine guided mode resonance waveguides with photonic crystal cavities. With optimized nanoantennas, we achieve Q factors exceeding 1,500 with Vm less than 0.1 ( λ / n air ) 3 . Each nanoantenna is individually addressable by free-space light and exhibits dipole-like scattering to the far-field. Resonator densities exceeding a million nanoantennas per cm2 can be achieved. As a proof-of-concept application, we show spectrometer-free, spatially localized, refractive-index sensing, and fabrication of an 8 mm × 8 mm VINPix array. Our platform provides a foundation for compact, densely multiplexed devices such as spatial light modulators, computational spectrometers and in situ environmental sensors.

Design and immunological evaluation of two-component protein nanoparticle vaccines for East Coast fever

Anna Lacasta, Hyung Chan Kim, Elizabeth Kepl, Rachael Gachogo, Naomi Chege, Rose Ojuok, Charity Muriuki, Stephen Mwalimu, Gilad Touboul, Ariel Stiber, Elizabeth Jane Poole, Nicholas Ndiwa, Brooke Fiala, Neil P King, Vishvanath Nene. “Design and immunological evaluation of two-component protein nanoparticle vaccines for East Coast fever.” Frontiers in Immunology, 13, 1015840 (2023).

Abstract: Nanoparticle vaccines usually prime stronger immune responses than soluble antigens. Within this class of subunit vaccines, the recent development of computationally designed self-assembling two-component protein nanoparticle scaffolds provides a powerful and versatile platform for displaying multiple copies of one or more antigens. Here we report the generation of three different nanoparticle immunogens displaying 60 copies of p67C, an 80 amino acid polypeptide from a candidate vaccine antigen of Theileria parva, and their immunogenicity in cattle. p67C is a truncation of p67, the major surface protein of the sporozoite stage of T. parva, an apicomplexan parasite that causes an often- fatal bovine disease called East Coast fever (ECF) in sub-Saharan Africa. Compared to I32-19 and I32-28, we found that I53-50 nanoparticle scaffolds displaying p67C had the best biophysical characteristics. p67C-I53-50 also outperformed the other two nanoparticles in stimulating p67C-specific IgG1 and IgG2 antibodies and CD4+ T-cell responses, as well as sporozoite neutralizing capacity. In experimental cattle vaccine trials, p67C-I53-50 induced significant immunity to ECF, suggesting that the I53-50 scaffold is a promising candidate for developing novel nanoparticle vaccines. To our knowledge this is the first application of computationally designed nanoparticles to the development of livestock vaccines.