Evidence map›Paper›PMID 42354722›Full record

ArticleMicromachines2026

Optimization and Parallelization of Sorting by Interfacial Tension (SIFT) for High-Throughput Metabolic Cell Sorting.

Aria Trivedi, Thomas Mathew, Matthew Shulman, Lakshmi Thangam, Pooja Dubey, Charlotte V Cohen, Kelsey Voss, Paul Abbyad

Abstract read
In one paragraph

Article in Micromachines, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Aria TrivediDepartment of Chemistry and Biochemistry, Santa Clara University, Santa Clara, CA 95053, USA.
Thomas MathewDepartment of Chemistry and Biochemistry, Santa Clara University, Santa Clara, CA 95053, USA.ORCID 0009-0009-4253-0580
Matthew ShulmanDepartment of Chemistry and Biochemistry, Santa Clara University, Santa Clara, CA 95053, USA.
Lakshmi ThangamDepartment of Chemistry and Biochemistry, Santa Clara University, Santa Clara, CA 95053, USA.
Pooja DubeyDepartment of Chemistry and Biochemistry, Santa Clara University, Santa Clara, CA 95053, USA.
Charlotte V CohenDepartment of Chemistry and Biochemistry, Santa Clara University, Santa Clara, CA 95053, USA.
Kelsey VossDepartment of Pharmacology, University of Virginia, Charlottesville, VA 22903, USA.ORCID 0000-0003-1732-2682
Paul AbbyadDepartment of Chemistry and Biochemistry, Santa Clara University, Santa Clara, CA 95053, USA.ORCID 0000-0002-8641-8533

Funding

Camille and Henry Dreyfus Foundation Henry Dreyfus Teacher-Scholar Awards ProgramDivision of Chemical, Bioengineering, Environmental, and Transport Systems 1751861
6 · The paper itself

Abstract

A systematic optimization of throughput and operational run time in Sorting by Interfacial Tension (SIFT) is presented. Reducing droplet size and enabling a broader distribution of droplet trajectories increased the number of droplets processed per sorting element, resulting in about a four-fold improvement in throughput from 30 to 125 droplets per second. Throughput was further enhanced through device parallelization, as demonstrated by devices incorporating two and four independent sorting regions. These configurations distribute droplets evenly across sorting elements. The elements exhibited comparable pH sorting thresholds, indicating similar flow conditions and drag forces within each region. Among the designs evaluated, the two-element configuration provided the optimal balance of throughput, specificity, robustness, and simplicity. It achieved maximum throughputs of about 250 droplets per second. In many biological applications, only 1 in 20-30 droplets are occupied to minimize multiple-cell occupancy, resulting in an effective sorting rate of approximately 8 cells per second. Throughput and pH sorting thresholds were preserved for two hours of continuous cell sorting. The improved platform was applied to examine the relationship between cellular glycolysis and iron homeostasis at the single-cell level in activated Jurkat cells. It revealed a subpopulation of highly glycolytic cells with significantly elevated iron uptake, consistent with prior reports linking iron regulation and T cell metabolism. Collectively, these advances expand the scale, stability, and biological applicability of SIFT. These advances facilitate large-scale functional studies and the capture of rare, metabolically distinct, cell populations.

Indexed as

cellscytometrydroplet microfluidicsglycolysismetabolismmicrofluidicspassive sortingsortingT-cells

Identifiers

PMID42354722
PMCPMC13303160

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.