Fluid-Squid: DIY Multiplexed Imaging of Cells and Tissues

Fluid-Squid: DIY Multiplexed Imaging of Cells and Tissues | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Fluid-Squid: DIY Multiplexed Imaging of Cells and Tissues View ORCID Profile John W. Hickey , Hongquan Li , Chiara Caraccio , You Yan , Kevin Marx , Peter Martin , Hope T Leng , John Fayiah , Elvis Towalid , Bonnie Dighero-Kemp , Garry P. Nolan , View ORCID Profile Manu Prakash , David R. McIlwain doi: https://doi.org/10.1101/2025.10.09.680291 John W. Hickey 1 Department of Biomedical Engineering, Duke University, Durham, NC, USA 2 Department of Microbiology and Immunology, Stanford University School of Medicine , Stanford, CA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for John W. Hickey For correspondence: john.hickey{at}duke.edu hongquan{at}cephla.com manup{at}stanford.edu Hongquan Li 3 Department of Bioengineering, Stanford University School of Medicine , Stanford, CA, USA 4 Cephla inc , Mountain View, CA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: john.hickey{at}duke.edu hongquan{at}cephla.com manup{at}stanford.edu Chiara Caraccio 2 Department of Microbiology and Immunology, Stanford University School of Medicine , Stanford, CA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site You Yan 4 Cephla inc , Mountain View, CA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kevin Marx 3 Department of Bioengineering, Stanford University School of Medicine , Stanford, CA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Peter Martin 4 Cephla inc , Mountain View, CA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Hope T Leng 3 Department of Bioengineering, Stanford University School of Medicine , Stanford, CA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site John Fayiah 5 Ludwig Maximillian University , München, Bavaria, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Elvis Towalid 6 University of Liberia , Monrovia, Liberia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Bonnie Dighero-Kemp 7 Dighero-Kemp Scientific Consulting LLC , Corvallis, OR, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Garry P. Nolan 8 Department of Pathology, Stanford University School of Medicine , Stanford, CA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Manu Prakash 3 Department of Bioengineering, Stanford University School of Medicine , Stanford, CA, USA 4 Cephla inc , Mountain View, CA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Manu Prakash For correspondence: john.hickey{at}duke.edu hongquan{at}cephla.com manup{at}stanford.edu David R. McIlwain 2 Department of Microbiology and Immunology, Stanford University School of Medicine , Stanford, CA, USA 9 Department of Microbiology and Immunology, University of Nevada Reno , Reno, NV, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Abstract Full Text Info/History Metrics Supplementary material Preview PDF Abstract Recent advances in multiplexed single-cell characterization have revolutionized our insight into cell biology, but many available technologies remain limited by high costs or a lack of customizability. To address these challenges, we developed Fluid-Squid, a cost-effective, quantitative imaging platform that integrates automated fluidics to support customizable, do-it-yourself multiplexed imaging workflows. Using Fluid-Squid, we successfully imaged fresh frozen human intestinal tissues with a 36-plex oligonucleotide-barcoded antibody panel and further demonstrated the feasibility of lyophilizing such multiplexed panels. We also adapted existing multiplexed imaging workflows to characterize individual cells to identify immune cell populations, phenotype, and antigen-specific cells from mouse splenocytes and human peripheral blood mononuclear cells (PBMCs) with a 39-antibody panel. To further expand its utility, we developed a barcoding strategy that allows for the pooling and simultaneous staining of multiple samples, reducing time, costs, and batch effects in single-cell experiments. This approach facilitated rapid titration to optimize antibody concentrations and assess the impact of various blood preparation methods on cell type retention. Overall, our work provides a new open-source framework for automated fluidics and microscopy in a flexible, cost-effective platform, empowering adaptable multiplexed characterization of both single cells and tissues. Introduction The past decade has seen remarkable advancements in single-cell biology, driven in part by the development of multiplexed imaging technologies. In particular, spatial-omics approaches—both spatial proteomic ( 1 ) and transcriptomic ( 2 ) methods—have driven insights into cellular function and organization within tissues for ( 3 ), cancer ( 4 ), and various diseases ( 5 , 6 ). These technologies enable simultaneous measurement of 50 or more markers at single-cell resolution. Consequently, single cell and spatial-omics techniques have been adopted by large-scale national and international tissue mapping consortia and have led to numerous commercialized systems. However, despite their commercialization, many of these systems remain prohibitively expensive and complex, limiting their accessibility or adaptability to modification in protocols. For instance, most of these platforms rely on automated fluidics for reagent exchange during multiplexing protocols ( 7 – 10 ) but do not lend well to adaptations to new procedures. While such automation increases throughput and reproducibility, it also adds substantial cost and reduces flexibility, as many commercial systems are designed to work with proprietary reagents and protocols. The lack of customizability in these systems constrains their use for researchers who require bespoke experimental setups, need to integrate new reagents, or want to establish new techniques. In addition to spatial technologies, single-cell suspension-based approaches such as mass cytometry (CyTOF) ( 11 ) and spectral flow cytometry ( 12 ) have emerged as powerful tools for profiling large numbers of markers (40+) on dissociated cells. However, like spatial-omics platforms, these methods require access to large, complex, and costly instrumentation, limiting their deployment to well-equipped labs or specialized core facilities. This reliance on expensive infrastructure presents a significant barrier, particularly for researchers conducting fieldwork in resource-limited settings, such as infectious disease outbreak studies such as Ebola that require the analysis of peripheral blood mononuclear cells (PBMCs) in situ ( 13 ). Given these challenges, there is a pressing need for accessible and customizable solutions that democratize multiplexed imaging technology. In response to this need, we developed Fluid-Squid, an open-source platform for multiplexed imaging that integrates automated fluidics with low-cost high-performance microscopy systems. Fluid-Squid is designed to empower researchers to conduct high-parameter imaging experiments in both tissue and single-cell contexts. With flexible open-source software control, Fluid-Squid enables do-it-yourself (DIY) multiplexed workflows offering researchers the flexibility to customize their experimental protocols, incorporate novel reagents, and perform cost-effective assays without the need for expensive large-footprint equipment. We demonstrate the versatility of Fluid-Squid through its application in characterizing tissue sections and immune cell populations, phenotypes, and antigen-specific cells in dissociated mouse splenocytes and human PBMCs. Additionally, we present a barcoding strategy that allows for the pooling and simultaneous staining of multiple samples, significantly reducing time, costs, and batch effects in single-cell experiments. This open-source platform bridges the gap between high-cost commercial systems and the need for customizable, scalable multiplexed imaging approaches, making advanced single-cell and tissue-level characterization rapidly accessible to a wider scientific community. Results Fluid-Squid: Low-cost, open-source platform for multiplexed imaging We recently developed Squid (Simplifying QUantitative Imaging platform Development and deployment) ( 14 ). This platform is a cost-effective, quantitative imaging platform featuring a comprehensive set of hardware and software for advanced widefield microscopy techniques like flat-field fluorescence excitation microscopy. Its open, modular design not only simplifies customization and programming for diverse experiments but also enhances accessibility, making advanced microscopy and deep learning-based methods more feasible for point-of-care or low-resource settings. Squid has now been field tested in more than a dozen applications including confocal microscopy, expansion microscopy and zero-gravity imaging ( 14 , 15 ). Building on this platform, we created Fluid-Squid, which integrates automated fluid handling ( Fig. 1A ) with automated imaging to enable multiplexed imaging. This setup is compact ( Fig. 1B ), requires minimal lab bench space, and includes dedicated software to control fluid flow from multiple ports ( Fig. 1C ). It is robust, portable, and has been validated through transport and deployment in partner institutions from the United States to Liberia where it was used ( Fig. 1D ) . A photo of a newer generation of the fluidics system coupled with a Squid spinning disk confocal is also shown in Fig. 1D . Overall, Fluid-Squid allows large-scale, multiplexed experiments, such as antibody-based imaging, to be performed autonomously with both fluidics and imaging fully integrated. Download figure Open in new tab Figure 1. Fluid-Squid a customizable, open setup which integrates automated fluid handling with automated imaging to enable multiplexed imaging. A) Schematic and core components CAD of the generation 1 fluidics system, B) Photo of the fluidics system coupled to a Squid inverted microscope, C) Graphical user interface of the fluidics system, D) Photo from trip to Liberia; the same instrument from Figure B set up at Liberia Institute for Biomedical Research; a newer generation of the fluidics system coupled to a Squid spinning disk confocal. The first-generation Squid fluidics system makes use of pressure driven flow to deliver reagents from sealed reservoirs to sample chambers like well plates. The pressure (up to 5 psi) is generated by an on-board piezo disc pump (Ventus). The same pump is also used to generate vacuum for aspiration. Pressure sensors are used for closed loop pressure control and for monitoring purposes (e.g. ensure reservoirs are properly sealed during automated pre-use check). A media isolation valve is used in the delivery path to ensure flow only starts after set pressure is reached for delivery of repeatable volumes. Flow sensors can be connected for additional monitoring, closed-loop control, and calibration purposes. Reservoirs for cycle-specific reagents are DNA LoBind eppendorf tubes connected to a custom machined manifold with o-rings and threaded adapters to lock the tubes in place. In a second generation system, we used a design similar to the ones reported in ( 16 , 17 ), where a syringe pump is used to pull reagents through a closed flow cell or aspirate reagents and dispense to open chambers. In this case the reagents can be stored in open instead of sealed reservoirs like well plates. The controller for the second-generation system also includes interfaces for multiple selector valves for more expandability (e.g., adding a selector valve for delivering reagents to multiple sample chambers). The Squid microscope is constructed as described previously in ( 14 ) except that 1) a motorized stage for well plate with 80 x 120 mm travel is used for well plates, 2) Squid 850 nm laser autofocus is integrated, 3) the 4 color laser engine is further optimized., and 4) instead of using the more expensive 561 nm line, a 530 nm diode laser is used for cy3. The total BOM (bill of materials) cost of the microscope and the fluidic system put together between 2021 and 2022 is within $30k. Multiplexed Imaging of tissues using Fluid-Squid Multiplexed imaging has transformed our understanding of the organization of tissues by enabling simultaneous profiling of dozens of markers at a single time ( 3 ). Computational tools that extract molecular information encoded as fluorescent signals, have enabled insight into cellular interactions and multicellular organization of tissues ( 18 – 24 ). We have previously developed CODEX (Co-Detection by indEXing) multiplexed imaging to profile cellular organization of tissue sections ( 10 , 25 – 27 ). This technique leverages oligonucleotide-barcoded antibodies and cycling on and off fluorophore-conjugated, complementary oligonucleotides to iteratively reveal and image dozens of antibodies within tissue sections. For the first time, we leveraged Fluid-Squid to perform CODEX multiplexed imaging with 36 DNA-barcoded antibodies within healthy human intestine tissue ( 18 ). We stained, imaged, and processed an area of the intestine of approximately 4.5 mm by 4.5 mm ( Fig. 2A ). The composite image of a subset of markers demonstrates consistent, high-quality staining across the entire section. Alpha smooth muscle actin effectively marked smooth muscle cells in both the muscularis externa and muscularis mucosa, cytokeratin identified epithelial cells in the mucosa, and CD56 highlighted nerve cells dispersed throughout the intestinal layers ( Fig. 2A ). Further analysis of the mucosal region revealed CD138 marking plasma cells, CD8 identifying CD8+ T cells, and Ki67 highlighting proliferating immune and epithelial cells ( Fig. 2B ). Similarly, other markers visualized aligned with known cell types and localizations within the intestine ( Fig. 2C-E ). These data indicate that Fluid-Squid is able to provide high-fidelity fluidics handling of cyclic fluid exchanges and integration with imaging of stained tissue sections required for CODEX. Download figure Open in new tab Figure 2. We used Fluid-Squid to perform CODEX multiplexed imaging of human intestine tissue. A) Imaging of a several mm 2 area of tissue with single-cell resolution. B-E) magnified regions showing distinct marker sets from the same experiment, 6 of 36 markers shown per panel. One key advantage of the Fluid-Squid system is its portability and flexibility. However, challenges arise in transporting and storing multiplexed imaging panels, particularly due to cold-chain storage requirements. To address this, we explored whether CODEX antibodies could be lyophilized and later reconstituted for staining. We tested this by comparing the performance of oligonucleotide-conjugated CD8 and B220 antibodies on mouse spleen sections: one group was used fresh, while the other was lyophilized and then reconstituted. The reconstituted antibodies performed comparably to those maintained in solution ( Fig. S1 ). This suggests that lyophilization enables CODEX antibody panels to be portable and standardized for use across different staining protocols. Application of CODEX multiplexed imaging to dissociated single cells We and others have mostly focused on using multiplexed imaging methods to analyze tissue samples ( 4 , 19 , 20 ). For example, our imaging of the human intestine or response to anti-cancer T cell therapies illustrates the powerful insights that microscopy-based approaches provide in understanding tissue architecture ( 18 , 19 ). Multiplexed analysis of single cells such as peripheral blood mononuclear cells (PBMCs) with techniques such as CyTOF ( 11 ) have also transformed our views into human immunology with measurement of the diversity, function, and composition of cells that relate to healthy and disease states. However, current techniques have several limitations. First, complex methods such as CyTOF or multispectral flow cytometry rely on expensive, large, elaborate setups. Second, other approaches such as single-cell RNA sequencing remain very expensive on a per-cell basis due to sequencing costs. Third, many of these multiplexed techniques lose important features such as cellular morphology or shape. To address these limitations, we developed a technique that applies CODEX multiplexed imaging to dissociated single-cell populations. Our approach follows the general workflow of traditional multiplexed techniques like CyTOF, where individual cell populations are isolated and stained with a cocktail of tagged antibodies. However, instead of analyzing the cells through microfluidics, they are fixed onto a glass surface and imaged using CODEX cyclic multiplexed imaging ( Fig. 3A ). Download figure Open in new tab Figure 3. We adapted CODEX multiplexed imaging to profile dissociated single cells like splenocytes or PBMCs. A) Our method works by staining cells in suspension with a cocktail of oligonucleotide-barcoded antibodies and fixing stained cells to slides or staining chambers and then performing cyclic imaging by adding and removing complementary, fluorophore-tagged oligonucleotides. B) Representative region from imaging mouse splenocytes across five cycles of imaging with only 3 of the 14 markers shown and C) in magnified regions with additional markers shown across all five cycles with D) cell segmentation masks. E-F) Using the same samples, we compared the E) percent cells positive for B220 from our CODEX profiling compared to F) splenocytes positive for B220 using flow cytometry. G) Correlation of data derived from either CODEX or flow cytometry across a number of markers and cell populations. H-L) Profiling of millions of human PBMCs across H) entire slides that I-J) when magnified and overlaid with markers can demonstrate different subpopulations of T cells with 6 of 36 markers shown. K) Quantification and gating of imaging data to identify specific cell type populations that are L) consistent across multiple different regions of the image. We first optimized and validated this technique using mouse splenocytes, applying a 14-antibody panel to cells in a 12-well glass plate. Manual cyclic fluidics were performed, and imaging was carried out with a commercial Keyence microscope (prior to adapting to Fluid-Squid as described in the following section). We efficiently captured fluorescent signals corresponding to distinct cell populations ( Fig. 3B ). We detect B cells (cycle 1), myeloid cells (cycle 2), neutrophils (cycle 3), T cells (cycle 4), and antigen-specific T cells (cycle 5) ( Fig. 3C ). By performing cell segmentation ( Fig. 3D ) we quantified CODEX fluorescent signals at the single-cell level and compared them with flow cytometry results. To accomplish this, the same splenocyte sample was divided, stained with fluorophore-labeled antibodies, and analyzed using flow cytometry. Within our CODEX imaging data, approximately 48% of cells were labeled as B220+ ( Fig. 3E ), closely matching the 44% identified by flow cytometry with PE-labeled anti-B220 ( Fig. 3F ). This analysis extended to 14 additional proteins shared between both assays, yielding a strong correlation (r 2 = 0.95) between populations identified by CODEX multiplexed imaging and flow cytometry ( Fig. 3G ). We further applied this method to PBMCs, placing the cells on a slide compatible with existing automated CODEX fluidics and imaging protocols designed for analyzing tissues. This also enabled imaging of large areas (~50 mm 2 ) of densely packed cells, resulting in data from hundreds of thousands of cells ( Fig. 3H ). We used a panel of 36 DNA-barcoded antibodies to delineate cell types and phenotypes, including distinct T cell phenotypes observed both in imaging data ( Fig. 3I-J ) and segmented single-cell data ( Fig. 3K ). A clear separation was evident between distinct cell type populations using markers like CD19 (B cells), CD3 (T cells), CD4 (CD4+ T cells), and CD8 (CD8+ T cells) ( Fig. 3K ). Continuous changes in protein expression, such as the decrease in CD45RA and simultaneous increase in CD45RO, were consistent with trends observed in other single-cell techniques like CyTOF ( 27 ) ( Fig. 3K ). Finally, we analyzed spatial consistency by dividing the image into three areas and applying the same gating strategy ( Fig. S2 ). Robust and consistent percentages of cell types were identified across different regions, indicating that our method reliably captures cell population information without introducing spatial batch effects ( Fig. 3L ). In total this represents a viable method for multiplexed characterization of dissociated single cells. Fluid-Squid for multiplexed imaging of single cells Having validated the CODEX approach on both mouse and human dissociated single cells, we proceeded to adapt the assay to the Fluid-Squid platform. First, we confirmed that the assay could be successfully performed with manual buffer exchanges, solely performing imaging using the Squid setup. The image quality obtained from Squid was comparable to that from a commercial fluorescent Keyence microscope ( Fig. S3A ). We reserved a set of PBMCs from the same donor and analyzed them with flow cytometry. Quantitatively, we observed similar proportions of CD3+ cells from Keyence or Squid images as those that were stained with alexa-647 anti-CD3, analyzed via flow cytometry ( Fig. S3B ). Next, we applied a full-scale 29-antibody panel to stain healthy human PBMCs and performed CODEX assay buffer exchanges and imaging in an automated manner using Fluid-Squid. Robust signals were observed from various cell populations within a subset of the total imaging area ( Fig. 4A ). We then performed cell segmentation across the entire imaging area, quantified the fluorescent signals, and conducted unsupervised clustering ( Fig. 4B ). This analysis identified 12 distinct cell types and phenotypes ( Fig. 4B-C ), associated with expected enrichment for cell type markers ( Fig. 4D ). By phenotyping in this manner, we were able to isolate individual populations, such as memory CD4+ T, B, and CD16+ NK cells, and examine specific marker distributions per cell visually. This allowed us to observe cell morphology, the overall intensity of each marker per cell, spatial distribution, cell polarization, and heterogeneity in cell type markers within the population ( Fig. 4E ). Download figure Open in new tab Figure 4. We used our Fluid-Squid set up to profile human PBMCs using a panel of 29 antibodies. A) A representative area of PBMCs containing various cell populations (image overlay for 5 of 29 markers used). B) UMAP plot of the cell types identified from multiplexed imaging of PBMCs, C) quantification within the sample, and D) dotplot highlighting marker expression on annotated cell types. E) Example of individual cell images for a few cells from the Memory CD4+ T cell population across all individual fluorescent channels used (including all the nuclear channels). Multiplexing sample analysis for large-scale single-cell studies A major advantage of profiling PBMCs with multiplexed imaging is the ability to characterize large numbers of cells simultaneously. Many studies that examine immune responses in humans using PBMCs will enlist large cohorts of donors. Because costs scale with the number of assays run, these studies can become costly. To address this issue, we aimed to develop a sample barcoding strategy that would allow pooling cells and samples for simultaneous staining and imaging, thereby reducing costs, time, and batch effects. We developed a barcoding strategy leveraging anti-CD45, a pan-immune cell marker ( Fig. 5A ), similar to those used in applications like CyTOF ( 28 ). To do this, we conjugated unique oligonucleotide barcodes to a distinct anti-CD45 antibody clone, generating unique set of anti-CD45 barcoding reagents. Individual cell populations were then stained with a unique anti-CD45 oligonucleotide combination before pooling for downstream staining. Download figure Open in new tab Figure 5. We demonstrate cell and sample-based barcoding for panel titration and different WBC preservation methods. A) We barcode cells within different samples by staining with a unique CD45 barcode. B) A representative image from four of the barcode channels used in PBMC experiments. C) Quantification of barcode fluorescent intensity per cell for two exclusive barcodes. D) Combinatorial barcoding enhances the number of barcode possibilities. E) Use of combinatorial barcoding showing a “common barcode” gated by barcode 1 and 2, and the “not-gate” used to plot barcode combination 3 and 4. F) We used this barcoding strategy to titrate our multiplexed antibody panel concentrations in one experiment. G) Quantification of signal allows us to determine the concentration to be used for each experiment. H) Different methods we tested preserving WBCs prior to analysis. I) CD16 versus CD66 measured within the same barcoded experiment for different PBMC methods and J) quantified. This approach allowed data from the pooled cells to be deconvoluted based on their unique CD45 barcodes ( Fig. 5B ). Cell segmentation of the data revealed that the barcoding created robustly labeled barcode channel positive and negative cells ( Fig. 5C ). Although single-antibody barcoding is effective and could theoretically generate a unique oligonucleotide barcode for each sample, this method is inefficient as it requires use of one barcode or imaging cycle per condition. To conserve imaging cycles while increasing barcoding capacity, we employed a combinatorial barcoding strategy by combining multiple uniquely barcoded anti-CD45 antibodies. ( Fig. 5D ). Using this strategy with six uniquely oligonucleotide-barcoded anti-CD45 antibodies, we generated 15 unique barcode combinations ( Fig. 5D ). We observed that samples were uniquely identified by double positive staining of corresponding anti-CD45 barcode channels ( Fig. 5E ), demonstrating the effectiveness of our combinatorial barcoding strategy in accurately distinguishing multiple samples within a pooled population. Titration of multiplexed imaging antibody panels In addition to allowing simultaneous analysis of multiple donors, our barcoding approach significantly streamlines the titration of highly multiplexed antibody panels. Titrating each antibody individually across samples can be time-consuming and costly. To address this, we employed our barcoding strategy to titrate our 40-plex antibody panel across four different concentrations in a single experiment. This revealed distinct shifts in the fluorescent signal quantified at the single-cell resolution across markers ( Fig. 5F ). For instance, we observed a marked reduction in CD4 signal as the concentration decreased from 1x to 0.3x, while CD8 showed no significant decrease until concentrations dropped from 0.3x to 0.1x ( Fig. 5F ). By quantifying these shifts across all markers, we were able to determine the optimal antibody concentration for staining PBMCs ( Fig. 5G ). Consequently, combinatorial sample barcoding can be accomplished through CD45 antibody staining and pooling of samples, eliminating batch effects and reducing costs per experiment. Sample preparation enables capture of representative immune cell populations from blood A persistent challenge in single-cell immune analyses is the loss of neutrophils, which are notoriously difficult to maintain during sample processing ( 29 ). We used our barcoding system to test in a single experiment five different immune cell sample preparation methods from the blood of healthy donors: gradient isolated PBMCs, gradient isolated PBMCs plus fixation, blood processed with white blood cell (WBC) filters, whole blood processed with ACK (Ammonium-Chloride-Potassium) lysing of red blood cells, and whole blood processed with smart tube cell fixation ( Fig. 5H ). Using our anti-CD45 barcoding approach, we pooled, stained with a panel of 39 antibodies, and imaged all conditions simultaneously. Part of this antibody panel we included two markers that are expressed by human neutrophils: CD16 and CD66. The results showed substantial differences between the sample preparation methods ( Fig. 5I ). Quantification of neutrophils as a percentage of total cells confirmed that the fixed and unfixed PBMC methods retained very few neutrophils, consistent with previous reports ( 30 , 31 ). In contrast, the other methods preserved significantly higher proportions of neutrophils ( Fig. 5J ). These differences extended to other immune cell populations as well ( Fig. S4 ). All samples were performed with WBCs that were isolated and stored at −20C. Since neutrophil percentages in healthy human blood range from 40-70%, the ACK lysis preparation strategy of −20C stored samples resulted in value within this range, where smart tube and WBC filters may exaggerate neutrophils abundance (possibly due to fragmentation) with values closer to 80% for the same donor. This data shows that using this approach we can successfully barcode human WBCs and perform quantitative assessments with different WBC preservation methods, including those that retain granulocyte fractions. Discussion While multiplexed imaging techniques have been developed and published to allow reproducibility by other labs ( 7 – 10 ), many of these approaches rely on expensive close-box microscopy solutions, limited or almost no automated fluidics capabilities, and lack of open-source flexibility for novel spatial-omics assay developers. These limitations have hindered the broader adoption of these technologies, particularly in resource-limited settings or for field applications, such as during infectious disease outbreaks. Fluid-Squid addresses these challenges by providing a low-cost, customizable, and open-source platform that integrates automated fluidics with advanced microscopy. Its versatility in imaging both tissues and cells, combined with its portability, offers significant advantages for labs looking to adopt or adapt this system for novel applications. For example, Fluid-Squid’s compatibility with non-slide-based imaging means much larger tissue sections can be analyzed, and cells cultured over extended periods of time can be characterized in situ. Furthermore, our demonstration that CODEX antibody panels can be lyophilized and reconstituted expands the potential for field-based and global applications, where cold-chain storage may not be feasible. In this study, we also advanced techniques by applying CODEX multiplexed imaging to dissociated single cells, specifically PBMCs and other WBC preservation methods, for the first time. This method offers multiplexing at scale while preserving critical features such as cell and nuclear morphology and the spatial localization of targets within individual cells—advantages that other single-cell approaches often lose. The integration of cellular barcoding within our system further enhances its utility by enabling the simultaneous profiling of multiple samples. This feature allowed for rapid titration of a 40+ antibody panel in a single experiment. Moreover, it enables reducing both time and costs while minimizing batch effects. This barcoding capability holds particular promise for large-scale cohort studies or longitudinal studies in fields such as immunology, cancer therapy, and infectious disease monitoring – both in lab and field settings. Furthermore, combining this barcoding strategy with emerging pooled perturbation methods, such as Perturb-View ( 32 ), opens up new possibilities for multiplexed perturbation experiments and large-scale functional studies. Similarly, this method could provide insights into cell-cell interactions and spatially defined ligands such as the immune synapse. Despite its many advantages, Fluid-Squid currently has some limitations as well. The fluidics system is presently restricted to 12 automated cycles per experiment, which may be insufficient for some highly multiplexed protocols. Efforts are underway to improve the fluidics system to handle more cycles and enhance its robustness across various buffer conditions, broadening its applicability beyond CODEX multiplexed imaging. Additionally, the current microscope setup supports only four-color imaging, but future iterations could incorporate spectral imaging technology to increase the number of channels and expand the system’s multiplexing capacity. Finally, while we have made significant progress in developing image analysis tools and workflows (10, 18, 19, 22-26, 33) , there is still room to improve the accessibility and flexibility of these toolsets. As Fluid-Squid continues to evolve, these analysis pipelines will be crucial for the seamless integration of image processing and data interpretation. In conclusion, Fluid-Squid is an open-source platform that empowers researchers to perform customizable, cost-effective multiplexed imaging of both single cells and tissues. By combining flexibility, scalability, and affordability, Fluid-Squid offers a powerful tool for generating new biological insights across a wide range of research fields. Our approach highlights the role of open-source hardware in biological explorations, paving the path towards an equitable distribution and creation of knowledge. Materials and Methods Fluid-Squid Setup Squid setup The Squid microscope used in this work includes motorized XY and Z stages, a Nikon 20x/0.75 Plan Apo objective, a 75 mm machine vision lens that serves as the tube lens, a Sony IMX226 camera, a 850 nm laser autofocus module, Chroma ZT405/488/561/640rpcxt-UF2 dichroic, Chroma ZET405/488/561/640xv2 excitation filter and Chroma ZET405/488/561/640mv2 emission filter, a custom 405/488/530/640 laser engine with SMA fiber connector and a critical illuminator using 400 um square core fiber. Fluidics setup The Fluidic system used in this work includes a custom PCB, a teensy 4.1 microcontroller, a TTP Ventus disc pump with driver daughter board, honeywell pressure sensors, Sensirion flow sensors, an IDEX 24 port selector valve (HT2425-915-3), clippard solenoid and media isolation valves, and custom machined interfaces for well plates and eppendorf tubes. Software for Fluid-Squid The python-based software for the first generation fluidics system is available at https://github.com/hongquanli/fluidics-controller-software . The latest microscope software with fluidics system integration can be found at https://github.com/cephla-lab/squid . CODEX antibody conjugation and panel creation Antibody conjugation Each antibody was conjugated to a unique oligonucleotide barcode using our previously established protocol ( 10 ), after which the tissues were stained with the antibody–oligonucleotide conjugates and we validated that the staining patterns matched the expected patterns already established for immunohistochemistry within positive control tissues of the intestine or tonsil. Otherwise, expected co-expression of markers in the case of staining of suspension cells was taken into account for validating separate cell type and phenotype antibodies. First, antibody–oligonucleotide conjugates were tested in low-plex fluorescence assays and the signal-to-noise ratio was also evaluated at this step, then they were tested all together in a single CODEX multicycle. Lyophilization of CODEX antibodies An aliquot of the CODEX antibodies for anti-CD8, -B220, -TCRb were taken and rapidly frozen by placing them in liquid nitrogen for 5-10 minutes. Tubes were then transferred to the lyophilizer and allowed to lyophilize until all moisture was removed over 24 hours. Antibodies were then stored at −80 C until used. Then antibodies were reconstituted with double-distilled water to the original constitution with thorough pipetting, spinning down, and allowing 15 minutes before use in staining. The same amount of this aliquot was used to stain one mouse spleen section as was the amount from the original CODEX antibodies that were maintained in the antibody stablizer solution at 4 C. Multiplexed Imaging of tissues Tissue sectioning and antibodies Tissues were individually frozen in OCT molds and then cut on the cryostat and sectioned at a width of 7 μm onto a well of a poly-L-lysine pre-coated 12-well glass-bottom plate. An antibody panel was previously constructed and optimized to image these tissues ( 18 ). Detailed information about the amount of antibodies used and exposure times is found in Supplementary Table 1. CODEX imaging The tissue arrays were then stained with the complete validated panel of CODEX antibodies and imaged ( 10 ). In brief, this entails cyclic stripping, annealing and imaging of fluorescently labeled oligonucleotides complementary to the oligonucleotide on the conjugate. After validation of the antibody–oligonucleotide conjugate panel, a test CODEX multiplexed assay was run, during which the signal-to-noise ratio was again evaluated, and the optimal dilution, exposure time and appropriate imaging cycle was evaluated for each conjugate. PBMC/Splenocyte Staining and Multiplexed Imaging PBMC preservation and Thawing Peripheral blood mononuclear cells (PBMCs) were cryopreserved in media containing 10% DMSO at a density of approximately 10 million cells per mL and stored in liquid nitrogen until use. Six vials of cryopreserved PBMCs (from the same donor) were thawed and processed for staining. PBMC vials were thawed in a 37°C water bath and transferred dropwise to pre-warmed cell thawing medium (RPMI + 10% FBS). Splenocyte staining Murine cells were obtained from adult mouse lymph nodes and spleens. Obtained cells were treated with ACK lysis buffer to lyse red blood cells, and lysates were filtered through cell strainers to isolate splenocytes. All following steps were performed with the same protocol as the PBMCs (names are exchangeable in the protocol). Cell Preparation and Fixation Cells were centrifuged at 500g for 5 minutes at room temperature, and the supernatant was carefully decanted. This wash step was repeated twice, first with cell thawing medium, then with Buffer S1. The cells were pooled, centrifuged, and resuspended in Buffer S1. Cells were fixed by resuspending in Buffer S1 containing 1.6% paraformaldehyde (PFA) for 10 minutes at room temperature. The fixed cells were then washed twice with Buffer S1 and prepared for barcoding. Cell Barcoding The fixed cells were blocked in S2 blocking buffer and split into six tubes for barcoding. Each cell suspension was incubated for 1 hour at room temperature with 1 µL of a unique CD45 barcoding antibody per 100 µL of cells. Following barcoding, the cells were washed three times with Buffer S2 and pooled. Pooling and Antibody Panel Staining A 10 µL aliquot of the pooled cells was diluted 1:10 in Buffer S2 and counted using a disposable hemocytometer. The total cell count was calculated, and 2.5 million cells were used for subsequent antibody panel staining. The barcoded cells were blocked in S2 blocking buffer for 15 minutes, followed by staining with a 20 µL CODEX antibody panel cocktail (per 2.5 million cells) for 1 hour at room temperature. Cells were washed three times with Buffer S2 after staining. The above procedure was also followed for cells which were not barcoded. Post-Stain Fixation and Plating Following staining, cells were fixed in Buffer S4 containing 1.6% PFA for 10 minutes and washed three times with PBS. Cells were resuspended in PBS and plated onto poly-L-lysine pre-coated 12-well glass-bottom plates. The plates were incubated at room temperature for 30-60 minutes to allow cells to adhere before a final fixation step with freshly prepared BS3 fixative. Cells were stored in Buffer S4 at 4°C until ready for imaging. Multiplexed Imaging Stained and fixed cells were imaged using a CODEX-Squid system, with buffer and oligonucleotide preparations carried out according to our prior protocol with CODEX multiplexed imaging with cyclic oligonucleotide. WBC preservation methods Four different methods of blood processing were tested to determine compatibility with CODEX-Octopi staining, focusing on the retention of granulocytes and other leukocytes. Blood samples from a single donor were processed using PBMC isolation, WBC filter processing, ACK lysis, and Smart Tube fixation. Following processing, cells were fixed and stored at 4°C in PBS prior to barcoding and staining. PBMC Isolation Blood was collected in sodium heparin CPT tubes and centrifuged at 1700 x g for 20 minutes at 21°C with slow acceleration and brake. The upper layer was transferred to 15 mL conical tubes, diluted with PBS, and centrifuged at 300 x g for 15 minutes at 4°C. Cells were washed in PBS, centrifuged, and fixed in 1.6% paraformaldehyde (PFA) for 10 minutes at room temperature. Fixed cells were washed, resuspended in PBS, and divided into aliquots. One aliquot was stored at 4°C, and the other was frozen at −20°C. WBC Filter Processing Blood was collected in EDTA vacutainers and passed through an Acrodisc WBC filter by gravity. The filter was washed with PBS, and WBCs were eluted by pressing PBS through the filter into a 15 mL conical tube. Cells were centrifuged at 300 x g for 10 minutes at 4°C, fixed in 1.6% PFA for 10 minutes at room temperature, and washed. The cell pellet was resuspended in PBS, divided into two aliquots, with one stored at 4°C and the other frozen at −20°C. ACK Lysis Processing Blood collected in EDTA vacutainers was mixed with ACK lysis buffer and incubated at room temperature for 3-5 minutes to lyse red blood cells. Cells were centrifuged at 300 x g for 15 minutes at 4°C, washed with PBS, and centrifuged again. The cell pellet was fixed in 1.6% PFA for 10 minutes at room temperature, washed, and resuspended in PBS. Aliquots were stored at 4°C and −20°C. Smart Tube Fixation Blood collected in sodium heparin vacutainers was mixed with Smart Tube Proteomic Stabilizer in cryovials and incubated for 10 minutes at room temperature. Cryovials were then frozen at −80°C. For thawing, samples were incubated in Thaw-Lyse buffer for 15 minutes at room temperature, centrifuged at 600 x g for 5 minutes at 22°C, and washed twice with Thaw-Lyse buffer. Cells were resuspended in PBS, divided into aliquots, and stored at 4°C and −0°C. Quantification of single-cell imaging datasets Fluid-Squid Image Processing For regular CODEX multiplexed imaging using the Akoya Phenocycler imager, we used the image processing of stitching, image registration, cycle concatenation, and background subtraction built into the commercial software. Cell Segmentation Fo CODEX multiplexed imaging data from the Akoya Phenocycler imager we used CellVisionSegmenter, a neural network R-CNN-based single-cell segmentation algorithm ( 34 ). The only parameter that was altered was the growth pixels of the nuclear mask, which we found experimentally to work best at a value of 3. Debarcoding and Gating We used cellengine.com platform to identify the barcoded populations of cells. In cases where we used one barcode, we would create gates for each marker positivity and exclude these cells from other cell barcodes. For double barcodes we selected only double-positive cells. In experiments we identified cell types based on binary expression of known marker expression in a similar way to the way we do this for flow cytometry or CyTOF. Unsupervised Clustering Nucleated cells were selected by gating Hoechst 1 and Hoechst 2 double-positive cells (across multiple cycles), followed by z-normalization of protein markers used for clustering (some phenotypic markers were not used in the unsupervised clustering). Cells positive (z > 1) for greater than 35 fluorescent markers were removed from the data. Then the data were overclustered Leiden-based clustering with the scanpy Python package (v.1.9.1). These processing steps were performed based on an in-depth analysis of normalization techniques and unsupervised clustering algorithms used for CODEX multiplexed imaging data of the intestine ( 24 ). Clusters were assigned a cell type on the basis of average cluster protein expression and the location within the image. Impure clusters were split or reclustered after mapping back to the original fluorescence images. Funding NIH T32 Fellowship (T32CA196585) (J.W.H.) American Cancer Society - Roaring Fork Valley Postdoctoral Fellowship (PF-20-032-01-CSM). (J.W.H.) Stanford Bio-X SIGF fellowship (H.L.). US Food and Drug Administration (FDA) Medical Countermeasures Initiative contracts 75F40120C00176 and HHSF223201610018C (D.R.M.) This article reflects the views of the authors and should not be construed as representing the views or policies of the FDA. M.P. acknowledges support from Moore Foundation, NSF Center for Cellular Construction, and Schmidt Innovation Fellows fund. Author contributions J.W.H., H.L., D.R.M., G.P.N., and M.P. Conceptualized, designed, and created the system and designed experiments. J.W.H., H.L., C.C., H.T.L, J.S.F., E.T., B.D-K. and D.R.M., performed experiments and collected data. H.L. designed and built the microscope, H.L., K.M., Y.Y., P.M. developed and iterated the fluidics systems, H.L., K.M. developed the image processing code, J.W.H., H.L., D.R.M., K.M., and C.C. analyzed data. J.W.H., H.L., D.R.M., G.P.N., and M.P. wrote and revised the manuscript. Declaration of interests H.L. and M.P. are co-founders of Cephla Inc. G.P.N. received research funding from Pfizer, Inc.; Vaxart, Inc.; Celgene, Inc.; and Juno Therapeutics, Inc. G.P.N. has equity in Akoya Biosciences, Inc. G.P.N. is a scientific advisory board member of Akoya Biosciences, Inc. Data and materials availability All data are available upon request by authors. Supplementary Materials Figs. S1 to S5 are provided in the Supplemental Materials document. Acknowledgments Funder Information Declared National Institutes of Health, https://ror.org/01cwqze88 , T32CA196585 American Cancer Society , PF-20-032-01-CSM Stanford University , Stanford Bio-X SIGF fellowship United States Food and Drug Administration , 75F40120C00176 , HHSF223201610018C Gordon and Betty Moore Foundation, https://ror.org/006wxqw41 NSF Center for Cellular Construction Schmidt Sciences , Innovation Fellows fund References 1. ↵ J. W. Hickey , E. K. Neumann , A. J. Radtke , J. M. Camarillo , R. T. Beuschel , A. Albanese , E. McDonough , J. Hatler , A. E. Wiblin , J. Fisher , Spatial mapping of protein composition and tissue organization: a primer for multiplexed antibody-based imaging . Nature methods 19 , 284 – 295 ( 2022 ). OpenUrl PubMed 2. ↵ L. Tian , F. Chen , E. Z. Macosko , The expanding vistas of spatial transcriptomics . Nature Biotechnology 41 , 773 – 782 ( 2023 ). OpenUrl CrossRef PubMed 3. ↵ S. Jain , L. Pei , J. M. Spraggins , M. Angelo , J. P. Carson , N. Gehlenborg , F. Ginty , J. P. Gonçalves , J. S. Hagood , J. W. Hickey , Advances and prospects for the Human BioMolecular Atlas Program (HuBMAP) . Nature cell biology 25 , 1089 – 1100 ( 2023 ). OpenUrl CrossRef PubMed 4. ↵ M.-A. Baertsch , G. P. Nolan , J. W. Hickey , Multicellular modules as clinical diagnostic and therapeutic targets . Trends in cancer 8 , 164 – 173 ( 2022 ). OpenUrl PubMed 5. ↵ W. Kuswanto , G. Nolan , G. Lu , in Seminars in immunopathology . ( Springer , 2023 ), vol. 45 , pp. 145 – 157 . OpenUrl 6. ↵ L. Liu , A. Chen , Y. Li , J. Mulder , H. Heyn , X. Xu , Spatiotemporal omics for biology and medicine . Cell 187 , 4488 – 4519 ( 2024 ). OpenUrl CrossRef PubMed 7. ↵ A. J. Radtke , C. J. Chu , Z. Yaniv , L. Yao , J. Marr , R. T. Beuschel , H. Ichise , A. Gola , J. Kabat , B. Lowekamp , IBEX: an iterative immunolabeling and chemical bleaching method for high-content imaging of diverse tissues . Nature protocols 17 , 378 – 401 ( 2022 ). OpenUrl PubMed 8. B. Kramer , J. Del Castillo , L. Pelkmans , G. Gut . ( 2023 ). 9. J.-R. Lin , B. Izar , S. Wang , C. Yapp , S. Mei , P. M. Shah , S. Santagata , P. K. Sorger , Highly multiplexed immunofluorescence imaging of human tissues and tumors using t-CyCIF and conventional optical microscopes . elife 7 , e31657 ( 2018 ). OpenUrl CrossRef PubMed 10. ↵ S. Black , D. Phillips , J. W. Hickey , J. Kennedy-Darling , V. G. Venkataraaman , N. Samusik , Y. Goltsev , C. M. Schürch , G. P. Nolan , CODEX multiplexed tissue imaging with DNA-conjugated antibodies . Nature protocols 16 , 3802 – 3835 ( 2021 ). OpenUrl PubMed 11. ↵ S. C. Bendall , E. F. Simonds , P. Qiu , E.-a. D. Amir , P. O. Krutzik , R. Finck , R. V. Bruggner , R. Melamed , A. Trejo , O. I. Ornatsky , Single-cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum . Science 332 , 687 – 696 ( 2011 ). OpenUrl Abstract / FREE Full Text 12. ↵ J. P. Robinson , Flow cytometry: past and future . Biotechniques 72 , 159 – 169 ( 2022 ). OpenUrl CrossRef PubMed 13. ↵ I. Núñez , J. Gillard , S. Fragoso-Saavedra , D. Feyaerts , L. Islas-Weinstein , A. A. Gallegos-Guzmán , U. Valente-García , J. Meyerowitz , J. D. Kelly , H. Chen , Longitudinal clinical phenotyping of post COVID condition in Mexican adults recovering from severe COVID-19: a prospective cohort study . Frontiers in Medicine 10 , 1236702 ( 2023 ). OpenUrl PubMed 14. ↵ H. Li , D. Krishnamurthy , E. Li , P. Vyas , N. Akireddy , C. Chai , M. Prakash , Squid: simplifying quantitative imaging platform development and deployment . BioRxiv , 2020.2012. 2028.424613 ( 2020 ). 15. ↵ T. Wareing , A. Stokes , K. E. Crompton , K. Murphy , J. Dawson , Y. F. Ugurluoglu , C. Richardson , H. Li , M. Prakash , A. J. Wollman , Microscopy with microfluidics in microgravity using FlightScope . npj Microgravity 11 , 1 – 8 ( 2025 ). OpenUrl PubMed 16. ↵ L. E. Borm , A. Mossi Albiach , C. C. Mannens , J. Janusauskas , C. Özgün , D. Fernández-García , R. Hodge , F. Castillo , C. R. Hedin , E. J. Villablanca , Scalable in situ single-cell profiling by electrophoretic capture of mRNA using EEL FISH . Nature Biotechnology 41 , 222 – 231 ( 2023 ). OpenUrl PubMed 17. ↵ K. H. Chen , A. N. Boettiger , J. R. Moffitt , S. Wang , X. Zhuang , Spatially resolved, highly multiplexed RNA profiling in single cells . Science 348 , aaa6090 ( 2015 ). OpenUrl Abstract / FREE Full Text 18. ↵ J. W. Hickey , W. R. Becker , S. A. Nevins , A. Horning , A. E. Perez , C. Zhu , B. Zhu , B. Wei , R. Chiu , D. C. Chen , Organization of the human intestine at single-cell resolution . Nature 619 , 572 – 584 ( 2023 ). OpenUrl CrossRef PubMed 19. ↵ J. W. Hickey , M. Haist , N. Horowitz , C. Caraccio , Y. Tan , A. J. Rech , M.-A. Baertsch , X. Rovira-Clave , B. Zhu , G. Vazquez , T cell-mediated curation and restructuring of tumor tissue coordinates an effective immune response . Cell Reports 42 , ( 2023 ). 20. ↵ M. Matusiak , J. W. Hickey , D. G. van IJzendoorn , G. Lu , L. Kidzinski , S. Zhu , D. R. Colburg , B. Luca , D. J. Phillips , S. W. Brubaker , Spatially Segregated Macrophage Populations Predict Distinct Outcomes In Colon Cancer . Cancer Discovery , ( 2024 ). 21. M. Strasser , D. L. Gibbs , P. Gascard , J. Bons , J. W. Hickey , C. M. Schürch , Y. Tan , S. Black , P. Chu , A. Ozkan , Concerted epithelial and stromal changes during progression of Barrett’s Esophagus to invasive adenocarcinoma exposed by multi-scale, multi-omics analysis . bioRxiv , 2023.2006. 2008.544265 ( 2023 ). 22. S. Chen , B. Zhu , S. Huang , J. W. Hickey , K. Z. Lin , M. Snyder , W. J. Greenleaf , G. P. Nolan , N. R. Zhang , Z. Ma , Integration of spatial and single-cell data across modalities with weakly linked features . Nature Biotechnology , 1 – 11 ( 2023 ). 23. J. W. Hickey , E. Agmon , N. Horowitz , T.-K. Tan , M. Lamore , J. B. Sunwoo , M. W. Covert , G. P. Nolan , Integrating multiplexed imaging and multiscale modeling identifies tumor phenotype conversion as a critical component of therapeutic T cell efficacy . Cell Systems 15 , 322 - 338 . e325 ( 2024 ). OpenUrl PubMed 24. ↵ J. W. Hickey , Y. Tan , G. P. Nolan , Y. Goltsev , Strategies for accurate cell type identification in CODEX multiplexed imaging data . Frontiers in Immunology 12 , 727626 ( 2021 ). OpenUrl PubMed 25. ↵ G. Lu , M. A. Baertsch , J. W. Hickey , Y. Goltsev , E. Forgó , S. Jiang , G. P. Nolan , A realtime GPU-accelerated parallelized image processor for large-scale multiplexed fluorescence microscopy data . Frontiers in Immunology 13 , 981825 ( 2022 ). OpenUrl PubMed 26. Y. Tan , T. N. Kempchen , M. Becker , M. Haist , D. Feyaerts , Y. Xiao , G. Su , A. J. Rech , R. Fan , J. W. Hickey , SPACEc: A Streamlined, Interactive Python Workflow for Multiplexed Image Processing and Analysis . bioRxiv , 2024.2006. 2029.601349 ( 2024 ). 27. ↵ J. Kennedy-Darling , S. S. Bhate , J. W. Hickey , S. Black , G. L. Barlow , G. Vazquez , V. G. Venkataraaman , N. Samusik , Y. Goltsev , C. M. Schürch , Highly multiplexed tissue imaging using repeated oligonucleotide exchange reaction . European Journal of Immunology 51 , 1262 – 1277 ( 2021 ). OpenUrl CrossRef PubMed 28. ↵ H. E. Mei , M. D. Leipold , A. R. Schulz , C. Chester , H. T. Maecker , Barcoding of live human peripheral blood mononuclear cells for multiplexed mass cytometry . The Journal of Immunology 194 , 2022 – 2031 ( 2015 ). OpenUrl PubMed 29. ↵ C. M. Hope , D. Huynh , Y. Y. Wong , H. Oakey , G. B. Perkins , T. Nguyen , S. Binkowski , M. Bui , A. Y. Choo , E. Gibson , Optimization of blood handling and peripheral blood mononuclear cell cryopreservation of low cell number samples . International journal of molecular sciences 22 , 9129 ( 2021 ). OpenUrl PubMed 30. ↵ A. K. McElroy , R. S. Akondy , D. R. Mcllwain , H. Chen , Z. Bjornson-Hooper , N. Mukherjee , A. K. Mehta , G. Nolan , S. T. Nichol , C. F. Spiropoulou , Immunologic timeline of Ebola virus disease and recovery in humans . JCI insight 5 , e137260 ( 2020 ). OpenUrl 31. ↵ D. Kotliar , A. E. Lin , J. Logue , T. K. Hughes , N. M. Khoury , S. S. Raju , M. H. Wadsworth , H. Chen , J. R. Kurtz , B. Dighero-Kemp , Single-cell profiling of Ebola virus disease in vivo reveals viral and host dynamics . Cell 183 , 1383 - 1401 . e1319 ( 2020 ). OpenUrl CrossRef PubMed 32. ↵ T. Kudo , A. M. Meireles , R. Moncada , Y. Chen , P. Wu , J. Gould , X. Hu , O. Kornfeld , R. Jesudason , C. Foo , Multiplexed, image-based pooled screens in primary cells and tissues with PerturbView . Nature Biotechnology , 1 – 10 ( 2024 ). 33. Y. Jain , L. L. Godwin , Y. Ju , N. Sood , E. M. Quardokus , A. Bueckle , T. Longacre , A. Horning , Y. Lin , E. D. Esplin , Segmentation of human functional tissue units in support of a Human Reference Atlas . Communications Biology 6 , 717 ( 2023 ). OpenUrl PubMed 34. ↵ M. Y. Lee , J. S. Bedia , S. S. Bhate , G. L. Barlow , D. Phillips , W. J. Fantl , G. P. Nolan , C. M. Schürch , CellSeg: a robust, pre-trained nucleus segmentation and pixel quantification software for highly multiplexed fluorescence images . BMC bioinformatics 23 , 46 ( 2022 ). OpenUrl CrossRef PubMed View the discussion thread. Back to top Previous Next Posted October 10, 2025. Download PDF Supplementary Material Email Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Fluid-Squid: DIY Multiplexed Imaging of Cells and Tissues Message Subject (Your Name) has forwarded a page to you from bioRxiv Message Body (Your Name) thought you would like to see this page from the bioRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share Fluid-Squid: DIY Multiplexed Imaging of Cells and Tissues John W. Hickey , Hongquan Li , Chiara Caraccio , You Yan , Kevin Marx , Peter Martin , Hope T Leng , John Fayiah , Elvis Towalid , Bonnie Dighero-Kemp , Garry P. Nolan , Manu Prakash , David R. McIlwain bioRxiv 2025.10.09.680291; doi: https://doi.org/10.1101/2025.10.09.680291 Share This Article: Copy Citation Tools Fluid-Squid: DIY Multiplexed Imaging of Cells and Tissues John W. Hickey , Hongquan Li , Chiara Caraccio , You Yan , Kevin Marx , Peter Martin , Hope T Leng , John Fayiah , Elvis Towalid , Bonnie Dighero-Kemp , Garry P. Nolan , Manu Prakash , David R. McIlwain bioRxiv 2025.10.09.680291; doi: https://doi.org/10.1101/2025.10.09.680291 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Bioengineering Subject Areas All Articles Animal Behavior and Cognition (7629) Biochemistry (17660) Bioengineering (13881) Bioinformatics (41910) Biophysics (21436) Cancer Biology (18576) Cell Biology (25480) Clinical Trials (138) Developmental Biology (13368) Ecology (19887) Epidemiology (2067) Evolutionary Biology (24302) Genetics (15598) Genomics (22482) Immunology (17726) Microbiology (40360) Molecular Biology (17163) Neuroscience (88534) Paleontology (666) Pathology (2830) Pharmacology and Toxicology (4821) Physiology (7637) Plant Biology (15129) Scientific Communication and Education (2045) Synthetic Biology (4290) Systems Biology (9817) Zoology (2269)