Back

Bead-based approaches to CRISPR diagnostics

Siddiqui, S. M.; Welch, N. L.; Nguyen, T. G.; Razmi, A.; Chang, T.; Senft, R.; Arizti-Sanz, J.; Mirhashemi, M. E.; Stirling, D. R.; Ackerman, C.; Cimini, B. A.; Blainey, P. C.; Sabeti, P. C.; Myhrvold, C.

2023-09-04 infectious diseases
10.1101/2023.09.03.23294926 medRxiv
Show abstract

CRISPR-based diagnostics have emerged as a promising tool for fast, accurate, and portable pathogen detection. There has been rapid progress in areas such as pre-amplification processes and CRISPR-related enzymes, but the development of reporter systems and reaction platforms has lagged behind. In this paper, we develop new bead-based techniques that can help fill both gaps. First, we develop a novel bead-based split-luciferase reporter system with improved sensitivity compared to standard fluorescence-based reporter design in CRISPR diagnostics. Second, we develop a highly deployable, bead-based platform capable of detecting nine distinct viral targets in parallelized, droplet-based reactions. We demonstrate the enhanced performance of both approaches on synthetic and clinical samples. Together, these systems represent new modalities in CRISPR diagnostics with increased sensitivity, speed, multiplexing, and deployability.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

1
Cell Reports Methods
○ 165 papers in training set
Top 0.1%
15.3%
Similar papers in this journal
2
Nature Biotechnology
○ 172 papers in training set
Top 0.1%
12.1%
Similar papers in this journal
3
Nature Communications
○ 5641 papers in training set
Top 18%
9.8%
Similar papers in this journal
4
Biosensors and Bioelectronics
○ 57 papers in training set
Top 0.1%
8.0%
Similar papers in this journal
5
Nature Biomedical Engineering
○ 47 papers in training set
Top 0.3%
3.5%
Similar papers in this journal
6
eLife
● 5828 papers in training set
Top 34%
3.3%
50% of probability mass above
7
ACS Synthetic Biology
● 287 papers in training set
Top 1%
2.7%
8
Life Science Alliance
● 285 papers in training set
Top 2%
2.5%
9
Science Advances
● 1243 papers in training set
Top 14%
2.5%
10
Cell
○ 431 papers in training set
Top 4%
2.4%
11
Proceedings of the National Academy of Sciences
● 2444 papers in training set
Top 25%
1.9%
12
Biotechnology and Bioengineering
○ 53 papers in training set
Top 0.4%
1.8%
13
ACS Sensors
● 49 papers in training set
Top 0.4%
1.8%
14
iScience
○ 1154 papers in training set
Top 17%
1.8%
15
Nucleic Acids Research
◐ 1281 papers in training set
Top 9%
1.5%
16
Med
○ 39 papers in training set
Top 0.3%
1.5%
17
Scientific Reports
○ 3612 papers in training set
Top 59%
1.4%
18
SLAS Discovery
○ 25 papers in training set
Top 0.2%
1.1%
19
The Journal of Molecular Diagnostics
○ 39 papers in training set
Top 0.4%
1.1%
20
eBioMedicine
○ 183 papers in training set
Top 4%
1.1%
21
Lab on a Chip
● 96 papers in training set
Top 0.8%
1.1%
22
PLOS Pathogens
● 820 papers in training set
Top 7%
1.1%
23
The CRISPR Journal
○ 39 papers in training set
Top 0.3%
1.1%
24
Nature Chemical Biology
○ 119 papers in training set
Top 3%
0.9%
25
Small Methods
○ 29 papers in training set
Top 0.7%
0.9%
26
Analytical Chemistry
● 218 papers in training set
Top 2%
0.9%
27
Microbiology Spectrum
● 469 papers in training set
Top 11%
0.6%
28
Cell Reports Medicine
○ 153 papers in training set
Top 6%
0.6%
29
PNAS Nexus
◐ 159 papers in training set
Top 4%
0.6%
30
Modern Pathology
○ 22 papers in training set
Top 0.5%
0.6%

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.