Evidence map›Paper›PMID 35332182›Full record

ArticleScientific reports2022

An open source toolkit for repurposing Illumina sequencing systems as versatile fluidics and imaging platforms.

Kunal Pandit, Joana Petrescu, Miguel Cuevas, William Stephenson, Peter Smibert, Hemali Phatnani, Silas Maniatis

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
1.1field-weighted citation impact, top 24% of its field
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

7 citing papers in PubMed, 13 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Massively Parallel Profiling of RNA-targeting CRISPR-Cas13d.bioRxiv : the preprint server for biology · 2023
    Article
  5. Article
  6. Article
  7. Review
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

7 authors at 2 institutions in 1 country.

Kunal Pandit *Technology Innovation Lab, New York Genome Center, New York, NY, USA. kpandit@nygenome.org.
Joana Petrescu *Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA.
Miguel CuevasDepartment of Neurology, Columbia University Irving Medical Center, New York, NY, USA.
William StephensonTechnology Innovation Lab, New York Genome Center, New York, NY, USA.
Peter SmibertTechnology Innovation Lab, New York Genome Center, New York, NY, USA.
Hemali PhatnaniDepartment of Neurology, Columbia University Irving Medical Center, New York, NY, USA. hphatnani@nygenome.org.
Silas ManiatisTechnology Innovation Lab, New York Genome Center, New York, NY, USA. smaniatis@nygenome.org.
New York Genome Center · USColumbia University Irving Medical Center · US

Funding

Center for Integrated Cellular Analysis - Valeria A. Sanchez EstradaRM1HG011014 · NHGRI · NEW YORK GENOME CENTER · PI LANDAU, DAN, SATIJA, RAHUL · 2020 to 2025
$22.1M
Integrating Spatial Multi-omics and Clinical Covariates to Identify Mechanisms of Disease in ALS-FTDR01NS118183 · NINDS · NEW YORK GENOME CENTER · PI Hemali Phatnani · 2020 to 2026
$4.7M
Connecting TDP-43 Pathology to the Molecular Profiles of NeurodegenerationRF1NS118570 · NINDS · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI GALE HAMMELL, MOLLY, PHATNANI, HEMALI · 2020 to 2020
$3.7M
Spatially Resolved Dynamics of Molecular Pathology and Intercellular Interactions in Amytrophic Lateral SclerosisR01NS116350 · NINDS · NEW YORK GENOME CENTER · PI Hemali Phatnani · 2020 to 2026
$3.6M
Elucidating changes in astrocyte subpopulations associated with resistance to Alzheimers Disease pathology in multi-ethnic cohortsR01AG066831 · NIA · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI MENON, VILAS · 2020 to 2024
$3.3M
Defining Immune Cell Heterogeneity in Human ALS and Mouse Model of the DiseaseR01NS117583 · NINDS · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI PRZEDBORSKI, SERGE E · 2020 to 2024
$2.4M
Connecting TDP-43 Pathology to the Molecular Profiles of NeurodegenerationR01NS118570 · NINDS · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI GALE HAMMELL, MOLLY, PHATNANI, HEMALI · 2024 to 2024
$880k
Clinicopathologic correlates of cognitive impairment in amyotrophic lateral sclerosis and frontotemporal dementia spectrum disorders (ALS-FTD)F30AG072766 · NIA · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI PETRESCU, JOANA · 2022 to 2024
$135k
NHGRI NIH HHS RM1 HG011014NIA NIH HHS F30 AG072766NIA NIH HHS R01 AG066831NINDS NIH HHS R01 NS116350NINDS NIH HHS R01 NS117583NINDS NIH HHS R01 NS118183NINDS NIH HHS R01 NS118570NINDS NIH HHS RF1 NS118570
6 · The paper itself

Abstract

Fluorescence microscopy is a key method in the life sciences. State of the art -omics methods combine fluorescence microscopy with complex protocols to visualize tens to thousands of features in each of millions of pixels across samples. These -omics methods require precise control of temperature, reagent application, and image acquisition parameters during iterative chemistry and imaging cycles conducted over the course of days or weeks. Automated execution of such methods enables robust and reproducible data generation. However, few commercial solutions exist for temperature controlled, fluidics coupled fluorescence imaging, and implementation of bespoke instrumentation requires specialized engineering expertise. Here we present PySeq2500, an open source Python code base and flow cell design that converts the Illumina HiSeq 2500 instrument, comprising an epifluorescence microscope with integrated fluidics, into an open platform for programmable applications without need for specialized engineering or software development expertise. Customizable PySeq2500 protocols enable experimental designs involving simultaneous 4-channel image acquisition, temperature control, reagent exchange, stable positioning, and sample integrity over extended experiments. To demonstrate accessible automation of complex, multi-day workflows, we use the PySeq2500 system for unattended execution of iterative indirect immunofluorescence imaging (4i). Our automated 4i method uses off-the-shelf antibodies over multiple cycles of staining, imaging, and antibody elution to build highly multiplexed maps of cell types and pathological features in mouse and postmortem human spinal cord sections. Given the widespread availability of HiSeq 2500 platforms and the simplicity of the modifications required to repurpose these systems, PySeq2500 enables non-specialists to develop and implement state of the art fluidics coupled imaging methods in a widely available benchtop system.

Indexed as

High-Throughput Nucleotide SequencingSoftwareAnimalsFecesMiceOptical ImagingWorkflow

Identifiers

PMID35332182
PMCPMC8948189
OpenAlexW4221002283

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.