scholarly journals Enhanced competitive protein exchange at the nano-bio interface enables ultra-deep coverage of the human plasma proteome

2022 ◽  
Author(s):  
Daniel Hornburg ◽  
Shadi Ferdosi ◽  
Moaraj Hasan ◽  
Behzad Tangeysh ◽  
Tristan R. Brown ◽  
...  

We have developed a scalable system that leverages protein nano interactions to overcome current limitations of deep plasma proteomics in large cohorts. Introducing proprietary engineered nanoparticles (NPs) into a biofluid such as blood plasma leads to the formation of a selective and reproducible protein corona at the particle protein interface, driven by the relationship between protein-NP affinity and protein abundance. Here we demonstrate the importance of tuning the protein to NP surface ratio (P/NP), which determines the competition between proteins for binding. We demonstrate how optimized P/NP ratio affects protein corona composition, ultimately enhancing performance of a fully automated NP based deep proteomic workflow (Proteograph). By limiting the available binding surface of NPs and increasing the binding competition, we identify 1.2 to 1.7x more proteins with only 1% false discovery rate on the surface of each NP, and up to 3x compared to a standard neat plasma proteomics workflow. Moreover, increased competition means proteins are more consistently identified and quantified across replicates, yielding precise quantification and improved coverage of the plasma proteome when using multiple physicochemically distinct NPs. In summary, by optimizing NPs and assay conditions, we capture a larger and more diverse set of proteins, enabling deep proteomic studies at scale.

2020 ◽  
pp. 2000948
Author(s):  
Claudia Corbo ◽  
Andrew A. Li ◽  
Hossein Poustchi ◽  
Gha Young Lee ◽  
Sabrina Stacks ◽  
...  

2018 ◽  
Vol 16 (1) ◽  
pp. 74-81 ◽  
Author(s):  
Olga I. Kiseleva ◽  
Elena A. Ponomarenko ◽  
Yulia A. Romashova ◽  
Ekaterina V. Poverennaya ◽  
Andrey V. Lisitsa

Background: Liquid chromatography coupled with targeted mass spectrometry underwent rapid technical evolution during last years and has become widely used technology in clinical laboratories. It offers confident specificity and sensitivity superior to those of traditional immunoassays. However, due to controversial reports on reproducibility of SRM measurements, the prospects of clinical appliance of the method are worth discussing. </P><P> Objective: The study was aimed at assessment of capabilities of SRM to achieve a thorough assembly of the human plasma proteome. </P><P> Method: We examined set of 19 human blood plasma samples to measure 100 proteins, including FDA-approved biomarkers, via SRM-assay. </P><P> Results: Out of 100 target proteins 43 proteins were confidently detected in at least two blood plasma sample runs, 36 and 21 proteins were either not detected in any run or inconsistently detected, respectively. Empiric dependences on protein detectability were derived to predict the number of biological samples required to detect with certainty a diagnostically relevant quantum of the human plasma proteome. </P><P> Conclusion: The number of samples exponentially increases with an increase in the number of protein targets, while proportionally decreasing to the logarithm of the limit of detection. Analytical sensitivity and enormous proteome heterogeneity are major bottlenecks of the human proteome exploration.


2009 ◽  
Vol 1216 (16) ◽  
pp. 3538-3545 ◽  
Author(s):  
Xiaoyang Zheng ◽  
Shiaw-lin Wu ◽  
Marina Hincapie ◽  
William S. Hancock

2021 ◽  
Author(s):  
Benjamin L de Bivort ◽  
Seaan M Buchanan ◽  
Kyobi J Skutt-Kakaria ◽  
Erika Gajda ◽  
Chelsea J O'Leary ◽  
...  

Individual animals behave differently from each other. This variability is a component of personality and arises even when genetics and environment are held constant. Discovering the biological mechanisms underlying behavioral variability depends on efficiently measuring individual behavioral bias, a requirement that is facilitated by automated, high-throughput experiments. We compiled a large data set of individual locomotor behavior measures, acquired from over 183,000 fruit flies walking in Y-shaped mazes. With this data set we first conducted a "computational ethology natural history" study to quantify the distribution of individual behavioral biases with unprecedented precision and examine correlations between behavioral measures with high power. We discovered a slight, but highly significant, left-bias in spontaneous locomotor decision-making. We then used the data to evaluate standing hypotheses about biological mechanisms affecting behavioral variability, specifically: the neuromodulator serotonin and its precursor transporter, heterogametic sex, and temperature. We found a variety of significant effects associated with each of these mechanisms that were behavior-dependent. This indicates that the relationship between biological mechanisms and behavioral variability may be highly context dependent. Going forward, automation of behavioral experiments will likely be essential in teasing out the complex causality of individuality.


2021 ◽  
Author(s):  
Rebecca L Pinals ◽  
Nicholas Ouassil ◽  
Jackson Travis Del Bonis-O'Donnell ◽  
Jeffrey W Wang ◽  
Markita P Landry

Engineered nanoparticles are advantageous for numerous biotechnology applications, including biomolecular sensing and delivery. However, testing the compatibility and function of nanotechnologies in biological systems requires a heuristic approach, where unpredictable biofouling often prevents effective implementation. Such biofouling is the result of spontaneous protein adsorption to the nanoparticle surface, forming the "protein corona" and altering the physicochemical properties, and thus intended function, of the nanotechnology. To better apply engineered nanoparticles in biological systems, herein, we develop a random forest classifier (RFC) trained with proteomic mass spectrometry data that identifies which proteins adsorb to nanoparticles. We model proteins that populate the corona of a single-walled carbon nanotube (SWCNT)-based optical nanosensor. We optimize the classifier and characterize the classifier performance against other models. To evaluate the predictive power of our model, we then apply the classifier to rapidly identify and experimentally validate proteins with high binding affinity to SWCNTs. Using protein properties based solely on amino acid sequence, we further determine protein features associated with increased likelihood of SWCNT binding: proteins with high content of solvent-exposed glycine residues and non-secondary structure-associated amino acids. Furthermore, proteins with high leucine residue content and beta-sheet-associated amino acids are less likely to form the SWCNT protein corona. The classifier presented herein provides an important tool to undertake the otherwise intractable problem of predicting protein-nanoparticle interactions, which is needed for more rapid and effective translation of nanobiotechnologies from in vitro synthesis to in vivo use.


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