Ex vivo and in vivo fluorescence detection and imaging of adenosine triphosphate.

BACKGROUND: Ex vivo and in vivo detection and imaging of adenosine triphosphate (ATP) is critically important for the diagnosis and treatment of diseases, which still remains challenges up to present. RESULTS: We herein demonstrate that ATP could be fluorescently detected and imaged ex vivo and in vivo. In particular, we fabricate a kind of fluorescent ATP probes, which are made of titanium carbide (TC) nanosheets modified with the ROX-tagged ATP-aptamer (TC/Apt). In the constructed TC/Apt, TC shows superior quenching efficiency against ROX (e.g., ~ 97%). While in the presence of ATP, ROX-tagged aptamer is released from TC surface, leading to the recovery of fluorescence of ROX under the 545-nm excitation. Consequently, a wide dynamic range from 1 µM to 1.5 mM ATP and a high sensitivity with a limit of detection (LOD) down to 0.2 µM ATP can be readily achieved by the prepared TC/Apt. We further demonstrate that the as-prepared TC/Apt probe is feasible for accurate discrimination of ATP in different samples including living cells, body fluids (e.g., mouse serum, mouse urine and human serum) and mouse tumor models. CONCLUSIONS: Fluorescence detection and imaging of ATP could be readily achieved in living cells, body fluids (e.g., urine and serum), as well as mouse tumor model through a new kind of fluorescent ATP nanoprobes, offering new powerful tools for the treatment of diseases related to abnormal fluctuation of ATP concentration.

Dual-Amplification Strategy-Based SERS Chip for Sensitive and Reproducible Detection of DNA Methyltransferase Activity in Human Serum.

Herein, we present a dual-amplification sensing strategy-based surface-enhanced Raman scattering (SERS) chip, which combines rolling circle amplification (RCA) and polyadenine (PolyA) assembly for sensitive and reproducible determination of the activity of M.SssI, a cytosine-guanine dinucleotide (CpG) methyltransferase (MTase). Typically, in the presence of M.SssI, RCA process is triggered, resulting in long, single-stranded DNA (ssDNA) fragments that are hybridized with thousands of Raman reporters of Cy3. Afterward, the resultant ssDNA fragments are conjugated to SERS-active substrates made of silver core-gold satellite nanocomposites-modified silicon wafer (Ag-Au NPs@Si), with the SERS enhancement factor of ∼5 × 106. The core-satellite nanostructures are assembled relied on the strong affinity of PolyA toward gold/silver surface. Of particular significance, the developed SERS chip displays an ultrahigh sensitivity with a low limit of detection (LOD) of 2.8 × 10-3 U/mL, which is around 2 orders of magnitude higher than most reported methods. In addition, the constructed chip features a broad detection range covering from 0.05 to 50 U/mL. Besides for the ultrahigh sensitivity and broad dynamic range, the chip also features good reproducibility (e.g., the relative standard deviation (RSD) is less than ∼12%). Taking advantages of these merits, the developed chip is feasible for accurate discrimination of M.SssI with various concentrations spiked in human serum samples with good recoveries ranging from 99.6% to 107%.

Setting Up a Surface-Enhanced Raman Scattering Database for Artificial-Intelligence-Based Label-Free Discrimination of Tumor Suppressor Genes.

The quality of input data in deep learning is tightly associated with the ultimate performance of the machine learner. Taking advantage of the unique merits of surface-enhanced Raman scattering (SERS) methodology in the collection and construction of a database (e.g., abundant intrinsic fingerprint information, noninvasive data acquisition process, strong anti-interfering ability, etc.), herein we set up a SERS-based database of deoxyribonucleic acid (DNA), suitable for artificial intelligence (AI)-based sensing applications. The database is collected and analyzed by silver nanoparticles (Ag NPs)-decorated silicon wafer (Ag NPs@Si) SERS chip, followed by training with a deep neural network (DNN). As proof-of-concept applications, three kinds of representative tumor suppressor genes, i.e., p16, p21, and p53 fragments, are readily discriminated in a label-free manner. Prominent and reproducible SERS spectra of these DNA molecules are collected and employed as input data for DNN learning and training, which enables selective discrimination of DNA target(s). The accuracy rate for the recognition of specific DNA target reached 90.28%.

A Graphene-Silver Nanoparticle-Silicon Sandwich SERS Chip for Quantitative Detection of Molecules and Capture, Discrimination, and Inactivation of Bacteria.

There currently exists increasing concerns on the development of a kind of high-performance SERS platform, which is suitable for sensing applications ranging from the molecular to cellular (e.g., bacteria) level. Herein, we develop a novel kind of universal SERS chip, made of graphene (G)-silver nanoparticle (AgNP)-silicon (Si) sandwich nanohybrids (G@AgNPs@Si), in which AgNPs are in situ grown on a silicon wafer through hydrofluoric acid-etching-assisted chemical reduction, followed by coating with single-layer graphene via a polymer-protective etching method. The resultant chip features a strong, stable, reproducible surface-enhanced Raman scattering (SERS) effect and reliable quantitative capability. By virtues of these merits, the G@AgNPs@Si platform is capable for not only molecular detection and quantification but also cellular analysis in real systems. As a proof-of-concept application, the chip allows ultrahigh sensitive and reliable detection of adenosine triphosphate (ATP), with a detection limit of ∼1 pM. In addition, the chip, serving as a novel multifunctional platform, enables simultaneous capture, discrimination, and inactivation of bacteria. Typically, the bacterial capture efficiency is 54% at 108 CFU mL-1 bacteria, and the antibacterial rate reaches 93% after 24 h of treatment. Of particular note, Escherichia coli and Staphylococcus aureus spiked into blood can be readily distinguished via the chip, suggesting its high potential for clinical applications.

Reusable Silicon-Based Surface-Enhanced Raman Scattering Ratiometric Aptasensor with High Sensitivity, Specificity, and Reproducibility.

Rapid, sensitive, and accurate detection of adenosine triphosphate (ATP), the primary energy molecule, is critical for the elucidation of its unique roles in cell signaling and many cellular reactions. Up to date, a major challenge is still remaining for fabricating surface-enhanced Raman scattering (SERS) aptamer sensors (aptasensors) suitable for accurate and reliable quantification of ATP. Herein, we develop the ratiometric silicon SERS aptasensor for ATP detection, which is made of uniform silver nanoparticles (Ag NPs)-modified silicon wafer (Ag NPs@Si), followed by the functionalization with double-stranded DNA (dsDNA I). The dsDNA I is formed by the aptamer and its complementary DNA (cDNA), which contains two independent segments (e.g., 5'-Cy3-labeled DNA-C1, 3'-ROX-labeled DNA-C2). In the presence of ATP, ROX-DNA-C2 is dissociated from dsDNA I due to the formation of aptamer/ATP complex, leading to the attenuation of ROX signals, and meanwhile, Cy3 signals remain constant ascribed to the formation of dsDNA II caused by the supplementation of aptamer. As a result, ratiometric signals of the ratio of ROX intensity to Cy3 intensity (IROX/ICy3) can be achieved. Of particular significance, the developed ATP aptasensor features excellent reproducibility [e.g., the relative standard deviation (RSD) is less than ∼4%, comparable or superior to that of previously reported aptasensors], ultrahigh sensitivity [e.g., the detection of limit (LOD) reaches 9.12 pM, lower than that of other reported ATP SERS aptasensors], as well as good recyclability (e.g., ∼9.3% of RSD values of ratiometric signals within three cycles).

Ultrasensitive discrimination of volatile organic compounds using a microfluidic silicon SERS artificial intelligence chip.

Current gaseous sensors hardly discriminate trace volatile organic compounds at the ppt level. Herein, we present an integrated platform for simultaneously enabling rapid preconcentration, reliable surface-enhanced Raman scattering, (SERS) detection and automatic identification of trace aldehydes at the ppt level. For rapid preconcentration, we demonstrate that the nozzle-like microfluidic concentrator allows the enrichment of rare gaseous analytes by five-fold in only 0.01 ms. The enriched gas is subsequently captured and detected by an integrated silicon-based SERS chip, which is made of zeolitic imidazolate framework-8 coated silver nanoparticles grown in situ on a silicon wafer. After SERS measurement, a fully connected deep neural network is built to extract faint features in the spectral dataset and discriminate volatile organic compound classes. We demonstrate that six kinds of gaseous aldehydes at 100 ppt could be detected and classified with an identification accuracy of ∼80.9% by using this platform.

Microfluidic synthesis of high-valence programmable atom-like nanoparticles for reliable sensing.

Synthesis of programmable atom-like nanoparticles (PANs) with high valences and high yields remains a grand challenge. Here, a novel synthetic strategy of microfluidic galvanic displacement (µ-GD) coupled with microfluidic DNA nanoassembly is advanced for synthesis of single-stranded DNA encoder (SSE)-encoded PANs for reliable surface-enhanced Raman scattering (SERS) sensing. Notably, PANs with high valences (e.g., n-valence, n = 12) are synthesized with high yields (e.g., >80%) owing to the effective control of interfacial reactions sequentially occurring in the microfluidic system. On the basis of this, we present the first demonstration of a PAN-based automatic analytical platform, in which sensor construction, sample loading and on-line monitoring are carried out in the microfluidic system, thus guaranteeing reliable quantitative measurement. In the proof-of-concept demonstration, accurate determination of tetracycline (TET) in serum and milk samples with a high recovery close to 100% and a low relative standard deviation (RSD) less than 5.0% is achieved by using this integrated analytical platform.