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ACKNOWLEDGMENTS
We thank C. Lu, S. Ravikumar, M. Austin, V. Kudrnova, S. Alansaari,
W. Pepe, A. Harris, and M. O. Ali for assistance with data collection
and video coding; L. Mullertz, S. Dablouk, and R. Van Dine for acting in
the videos; E. Chen for assistance with trimming videos, recruitment,
and editing drafts of this manuscript; and L. Schulz, T. Ullman,
S. Liu, H. Olson, L. Powell, and M. Hung for comments on earlier
drafts of the manuscript. Statistical support was provided by data
science specialist S. Worthington at the Institute for Quantitative
Social Science, Harvard University.Funding:NIH National Research
Service Award 1F32HD096829 (A.J.T.); Patrick J. McGovern
Foundation (R.S.); Guggenheim Foundation (R.S.); Social Sciences
and Humanities Research Council Doctoral Fellowship 752-2020-0474(B.W.); NSF Center for Brains Minds and Machines award CCF-1231216
(E.S., A.J.T., B.W., R.S.); Siegel Foundation award S4881 (E.S.)
Author contributions:Conceptualization: A.J.T., R.S., E.S., D.N.
Methodology: A.J.T., R.S., E.S., B.W., D.N. Formal Analysis: A.J.T.
Resources: E.S., R.S. Investigation: A.J.T. Visualization: A.J.T. Funding
acquisition: A.J.T., R.S., E.S. Project administration: A.J.T.
Supervision: A.J.T., R.S., E.S. Writing–original draft: A.J.T., R.S.
Writing–review and editing: A.J.T., R.S., D.N., B.W., E.S.Competing
interests:The authors declare that they have no competing
interests.Data and materials availability:All data are available on
OSF (https://osf.io/a8htx). A subset of participant videos are
available on Databrary, https://nyu.databrary.org/volume/1253.SUPPLEMENTARY MATERIALS
science.org/doi/10.1126/science.abh1054
Materials and Methods
Supplementary Text
Fig. S1
References ( 43 – 53 )
16 February 2021; accepted 7 December 2021
10.1126/science.abh1054RESEARCH TECHNOLOGY
High-speed fluorescence imageÐenabled cell sorting
Daniel Schraivogel^1 , Terra M. Kuhn^2 †, Benedikt Rauscher^1 †, Marta Rodríguez-Martínez^1 †,
Malte Paulsen^3 ‡, Keegan Owsley^4 , Aaron Middlebrook^4 , Christian Tischer^5 , Beáta Ramasz^3 ,
Diana Ordoñez-Rueda^3 , Martina Dees^2 , Sara Cuylen-Haering^2 , Eric Diebold^4 , Lars M. Steinmetz1,6,7*
Fast and selective isolation of single cells with unique spatial and morphological traits remains a
technical challenge. Here, we address this by establishing high-speed image-enabled cell sorting (ICS),
which records multicolor fluorescence images and sorts cells based on measurements from image
data at speeds up to 15,000 events per second. We show that ICS quantifies cell morphology and localization
of labeled proteins and increases the resolution of cell cycle analyses by separating mitotic stages. We
combine ICS with CRISPR-pooled screens to identify regulators of the nuclear factorkB (NF-kB) pathway,
enabling the completion of genome-wide image-based screens in about 9 hours of run time. By assessing
complex cellular phenotypes, ICS substantially expands the phenotypic space accessible to cell-sorting
applications and pooled genetic screening.
F
luorescence microscopy and flow cytom-
etry are instrumental technologies used
in almost all areas of biological and bio-
medical research. Although flow cytomet-
ric cell sorting simplifies the isolation of
cells in a rapid, sensitive, and high-throughput
manner, it is limited to a low-dimensional
parameter space and lacks subcellular resolu-
tion ( 1 ). This method is therefore unable to
capture phenotypes associated with processes
involving varying signal localization, such
as protein trafficking, cellular signaling, or
protein mislocalization during disease ( 2 , 3 ).
Fluorescence microscopy, on the other hand,
enables high-resolution readouts of cellular
morphology and protein localization but lacks
the ability to isolate cells with specific pheno-
types at high speed ( 4 ). Combining the spatial
resolution of fluorescence microscopy with
flow cytometric cell sorting has broad implica-
tions and would inspire new experimental
strategies through the rapid identification and
isolation of cells with specific (sub)cellular
phenotypes.
Although flow- and microfluidics-based cyto-
meters with imaging capabilities have been
developed, these approaches were unable to
sort cells, came with drastically reduced through-
put, or depended on nonhuman interpretable
pattern recognition from raw data without im-
age reconstruction ( 5 – 14 ). Furthermore, image-
enabled cell sorting has so far relied on tech-
nically challenging and custom-built solutions.
To date, no system has been developed that
integrates traditional flow cytometry and mi-
croscopy, operates at speeds compatible with
genetic screening approaches and short-liveddynamic phenotypes, and can be operated in
nonspecialized laboratories.
Here, we present a fully integrated image-
enabled cell sorter (ICS) by combining (i) fluo-
rescence imaging using radiofrequency–tagged
emission (FIRE), a fast fluorescence imaging
technique ( 15 ), with (ii) a traditional cuvette-
based droplet sorter and (iii) new low-latency
signal processing and sorting electronics (Fig. 1,
A and B; for a detailed description and char-
acterization of ICS technology, please see the
materials and methods and fig. S1; for a de-
scription of the performance attributes of ICS,
please see the supplementary text). To enable
blur-free imaging at a high nominal flow speed
of 1.1 m/s, ICS uses the FIRE approach to pro-
duce an array of 104 laser spots across 60mm
within the core stream of the sorter cuvette,
each modulated at a unique radiofrequency
(Fig. 1A). The array of spots excites modulated
fluorescent and scattered light from particles
or cells as they flow through the optical inter-
rogation region in the cuvette. Emitted light
is collected, and the signal output is digit-
ized and processed using low-latency, field-
programmable gate arrays, allowing real-time
image analysis and image-derived sort de-
cisions. This is different from other image-
enabled flow cytometers without cell-sorting
capabilities ( 5 – 8 , 11 – 13 ) (see the supplemen-
tary text for a comparison between technolo-
gies). To reconstruct a row of pixels from the
FIRE signal for visualization of the event, the
amplitude of the signal at a unique modula-
tion frequency is assigned to a pixel value in a
specific horizontal coordinate in the cuvette;
in the direction of flow, the pixels are assigned
a vertical location based on their temporal
value, which forms a two-dimensional image
of an event (Fig. 1A). The system collects
scatter and fluorescent signals, as well as a
light loss signal (analogous to bright-field
images produced by traditional microscopes),
which allows visualization of events in realSCIENCEscience.org 21 JANUARY 2022¥VOL 375 ISSUE 6578 315
(^1) Genome Biology Unit, European Molecular Biology
Laboratory (EMBL), Heidelberg, Germany.^2 Cell Biology
and Biophysics Unit, EMBL, Heidelberg, Germany.^3 Flow
Cytometry Core Facility, EMBL, Heidelberg, Germany.^4 BD
Biosciences, San Jose, CA, USA.^5 Advanced Light
Microscopy Core Facility, EMBL, Heidelberg, Germany.
(^6) Department of Genetics, Stanford University School of
Medicine, Stanford, CA, USA.^7 Stanford Genome
Technology Center, Palo Alto, CA, USA.
*Corresponding author. Email: [email protected] (L.M.S.);
[email protected] (E.D.); [email protected] (S.C.-H.)
†These authors contributed equally to this work
‡Present address: Novo Nordisk Foundation Center for Stem Cell
Medicine, reNEW, Copenhagen, Denmark.
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