Evidence map›Paper›PMID 41589505›Full record

ArticleIUCrJ2026

Automated microbatch-under-oil phase diagrams to rationalize serial crystallography sample preparation.

Jack Stubbs, Courtney J Tremlett, Abigail Waitman, Nicholas J Harmer, Allen M Orville, Ivo Tews, Stefan Kolek, Patrick D Shaw Stewart

Abstract read
In one paragraph

Article in IUCrJ, 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.

Jack StubbsSchool of Biological Sciences, Faculty of Environmental and Life Sciences, University of Southampton, Southampton SO17 1BJ, United Kingdom.ORCID 0000-0002-3788-1687
Courtney J TremlettLiving Systems Institute, University of Exeter, Stocker Road, Exeter EX4 4QD, United Kingdom.ORCID 0000-0001-7683-1124
Abigail WaitmanSchool of Biological Sciences, Faculty of Environmental and Life Sciences, University of Southampton, Southampton SO17 1BJ, United Kingdom.ORCID 0009-0003-4167-1214
Nicholas J HarmerLiving Systems Institute, University of Exeter, Stocker Road, Exeter EX4 4QD, United Kingdom.ORCID 0000-0002-4073-0505
Allen M OrvilleDiamond Light Source, Harwell Science and Innovation Campus, Didcot OX11 0DE, United Kingdom.ORCID 0000-0002-7803-1777
Ivo TewsSchool of Biological Sciences, Faculty of Environmental and Life Sciences, University of Southampton, Southampton SO17 1BJ, United Kingdom.ORCID 0000-0002-4704-1139
Stefan KolekDouglas Instruments Ltd, East Garston, Hungerford RG17 7HD, United Kingdom.ORCID 0009-0003-0113-4721
Patrick D Shaw StewartDouglas Instruments Ltd, East Garston, Hungerford RG17 7HD, United Kingdom.ORCID 0000-0002-0184-4083

Funding

Biotechnology and Biological Sciences Research Council BB/M009122/1Biotechnology and Biological Sciences Research Council BB/T008768/1Biotechnology and Biological Sciences Research Council BB/Y009339/1Royal Society of Chemistry RSWFR2Wellcome Trust 210734/Z/18/Z
6 · The paper itself

Abstract

Serial crystallography relies on the reproducible production of high-density suspensions of microcrystals, yet sample optimization remains a resource-intensive bottleneck. While phase diagrams provide a theoretical framework for controlling crystal size and number, experimental mapping is traditionally hindered by relatively high sample consumption. We present an automated microbatch-under-oil crystallization approach that rapidly maps phase boundaries using only 15-60 µl (∼0.15-3.8 mg) of protein. While this workflow is ideally suited for refining existing hits, it serves as a standalone platform for characterizing the crystallization landscape of new protein targets. The power of this approach lies in the integration of three distinct strategies that exploit the stable chemical environment of microbatch-under-oil. Firstly, we utilize an ingenious diagonal sampling strategy that traverses the phase boundary parallel to the solubility curve by systematically varying protein-to-precipitant ratios, identifying primary nucleation zones with far greater efficiency than traditional orthogonal grids. Secondly, we employ a linked variation of multiple precipitants to reveal morphology-specific regions, such as the rod versus plate transitions crucial for time-resolved experiments. Finally, we incorporate automated seed-stock titration to precisely define the metastable zone, enabling the predictive rescue of nucleation-limited systems. The synergy of these three strategies enables the systematic decoupling of nucleation from growth, providing a rational route to optimize microcrystal density, size and lattice order. Crucially, by eliminating the evaporation-related variables inherent in vapor diffusion, this method ensures that the chemical coordinates identified during screening remain constant during scale-up to larger volumes. This workflow transforms empirical serial crystallography sample preparation into a rational, reproducible and highly efficient process applicable to both the optimization of known conditions and the de novo development of microcrystal suspensions, tailored to the rigorous demands of modern serial diffraction experiments.

Indexed as

microbatch-under-oilmicrocrystalsoptimizationphase diagramssample preparationseedingserial crystallography

Identifiers

PMID41589505
PMCPMC12951833

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.