Periodic Reporting for period 1 - 2DMAP (2D Materials Assisted Nanoscopic Mapping of Proton fluxes in living cells)
Período documentado: 2022-12-01 hasta 2024-11-30
The main goal of this project is to develop a method to map proton fluxes across biological membranes with single-proton-pump resolution for cancer diagnostics and treatment, taking advantage of super-resolution microscopy and the recent discovery of the proton-activated photon-emitting defects in a 2-dimensional material, called hexagonal boron nitride.
Proton fluxes through various transmembrane proton channels are universally vital for all living creatures. A proton gradient across the membrane stores the potential energy, powering cell activities with energy conversion by enzymes. Abnormal transmembrane proton fluxes are associated with various diseases. Over-expression of transmembrane proton pumps (hHv1) has been found in colorectal tumor tissues and metastatic breast cancer cells5. Research suggests that the over-expression of hHv1 proton channels may foster cancer spreading by enhancing the migratory ability and invasion of the cells.
Proton fluxes through various transmembrane proton channels are universally vital for all living creatures. A proton gradient across the membrane stores the potential energy, powering cell activities with energy conversion by enzymes. Abnormal transmembrane proton fluxes are associated with various diseases. Over-expression of transmembrane proton pumps (hHv1) has been found in colorectal tumor tissues and metastatic breast cancer cells5. Research suggests that the over-expression of hHv1 proton channels may foster cancer spreading by enhancing the migratory ability and invasion of the cells.
We have investigated the fabrication of defects in hexagonal boron nitride (hBN) and explored the photoluminescence emission behavior of these defects in aqueous buffer solutions. Preliminary cell-culturing experiments on hBN were successfully conducted as planned. However, during the course of our research, we discovered that small biomolecules in buffer solutions can also activate emissions from hBN defects, in addition to the previously reported proton-induced activation. This unexpected finding led us to shift our focus toward understanding molecule-activated defect emissions using spectrally-resolved PAINT microscopy.
Building on this insight, we combined molecule-activated defect emissions with spectrally-resolved Point Accumulation for Imaging in Nanoscale Topography (sPAINT) super-resolution microscopy to visualize these interactions. This multidimensional approach enabled the simultaneous acquisition of spatial, spectral, and temporal information at the single-molecule level. By applying machine learning algorithms, we identified molecular fingerprints and demonstrated the detection and classification of various fundamental biomolecular building blocks, including lipids, nucleotides, amino acids, and glycans.
Building on this insight, we combined molecule-activated defect emissions with spectrally-resolved Point Accumulation for Imaging in Nanoscale Topography (sPAINT) super-resolution microscopy to visualize these interactions. This multidimensional approach enabled the simultaneous acquisition of spatial, spectral, and temporal information at the single-molecule level. By applying machine learning algorithms, we identified molecular fingerprints and demonstrated the detection and classification of various fundamental biomolecular building blocks, including lipids, nucleotides, amino acids, and glycans.
By exploiting the specific interactions between biomolecules and surface defects in hexagonal boron nitride (hBN), we discovered that small biomolecules—including lipids, nucleotides, amino acids, and glycans—can selectively activate photon emissions from defects on the hBN surface under aqueous conditions. These emissions are highly sensitive to the molecular structure, enabling us to extract distinct optical signatures specific to individual biomolecules.
Building on this discovery, we developed a novel label-free detection platform using sPAINT,. This approach captures spatial, spectral, and temporal information of the transient molecule-activated defect emissions. The integration of machine learning algorithms into the analysis pipeline allowed us to classify these emissions and identify different biomolecules with high precision. As a proof-of-concept, we successfully demonstrated the identification of five amino acids at the single-molecule level without requiring labeling or external modification.
It provides a powerful tool for advancing single-molecule biophysics by enabling the study of biomolecules in their native states. The label-free nature of the platform simplifies sample preparation, making it particularly valuable for biomedical diagnostics, environmental monitoring, and pharmaceutical research. Additionally, the work highlights the unique properties of hBN as a versatile and sensitive platform for advanced biomolecular sensing, opening new avenues for its application in cutting-edge research and diagnostics. This combination of innovative methodology and broad applicability positions the work as a transformative step in the field of molecular sensing.
Building on this discovery, we developed a novel label-free detection platform using sPAINT,. This approach captures spatial, spectral, and temporal information of the transient molecule-activated defect emissions. The integration of machine learning algorithms into the analysis pipeline allowed us to classify these emissions and identify different biomolecules with high precision. As a proof-of-concept, we successfully demonstrated the identification of five amino acids at the single-molecule level without requiring labeling or external modification.
It provides a powerful tool for advancing single-molecule biophysics by enabling the study of biomolecules in their native states. The label-free nature of the platform simplifies sample preparation, making it particularly valuable for biomedical diagnostics, environmental monitoring, and pharmaceutical research. Additionally, the work highlights the unique properties of hBN as a versatile and sensitive platform for advanced biomolecular sensing, opening new avenues for its application in cutting-edge research and diagnostics. This combination of innovative methodology and broad applicability positions the work as a transformative step in the field of molecular sensing.