O material que encolhe a luz permite que o microsca³pio comum veja em super resolução
Os pesquisadores desenvolveram uma tecnologia que melhora a resolua§a£o de um microsca³pio de luz comum para que possa ser usado para observar diretamente estruturas e detalhes mais finos em células vivas.
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)
O material montado no palco de um microsca³pio invertido. Crédito: Junxiang Zhao
Os engenheiros elanãtricos da Universidade da Califórnia em San Diego desenvolveram uma tecnologia que melhora a resolução de um microsca³pio de luz comum para que possa ser usado para observar diretamente estruturas e detalhes mais finos em células vivas.
A tecnologia transforma um microsca³pio de luz convencional no que échamado de microsca³pio de super-resolução. Envolve um material especialmente projetado que encurta o comprimento de onda da luz a medida que ilumina a amostra - essa luz encolhida éo que essencialmente permite ao microsca³pio obter imagens em resolução mais alta.
"Este material converte luz de baixa resolução em luz de alta resolução", disse Zhaowei Liu, professor de engenharia elanãtrica e de computação na UC San Diego. "a‰ muito simples e fa¡cil de usar. Basta colocar uma amostra no material e, em seguida, colocar tudo sob um microsca³pio normal - nenhuma modificação extravagante necessa¡ria."
O trabalho, que foi publicado na Nature Communications , supera uma grande limitação dos microsca³pios de luz convencionais: a baixa resolução. Os microsca³pios de luz são aºteis para obter imagens de células vivas , mas não podem ser usados ​​para ver nada menor. Os microsca³pios de luz convencionais tem um limite de resolução de 200 nana´metros, o que significa que quaisquer objetos mais pra³ximos do que essa distância não sera£o observados como objetos separados. E embora existam ferramentas mais poderosas, como microsca³pios eletra´nicos, que tem a resolução para ver estruturas subcelulares, eles não podem ser usados ​​para criar imagens de células vivas porque as amostras precisam ser colocadas dentro de uma ca¢mara de va¡cuo.
"O maior desafio éencontrar uma tecnologia que tenha resolução muito alta e também seja segura para células vivas ", disse Liu.
A tecnologia que a equipe de Liu desenvolveu combina os dois recursos. Com ele, um microsca³pio de luz convencional pode ser usado para obter imagens de estruturas subcelulares vivas com resolução de até40 nana´metros.
A tecnologia consiste em uma lâmina de microsca³pio revestida com um tipo de material que reduz a luz, chamado metamaterial hiperba³lico. a‰ feito de camadas alternadas de prata e vidro de salica com nana´metros de espessura. Conforme a luz passa, seus comprimentos de onda encurtam e se espalham para gerar uma sanãrie de padraµes pontilhados aleata³rios de alta resolução. Quando uma amostra émontada na lâmina, ela éiluminada de maneiras diferentes por essa sanãrie de padraµes de luz pontilhada. Isso cria uma sanãrie de imagens de baixa resolução, que são todas capturadas e depois reunidas por um algoritmo de reconstrução para produzir uma imagem de alta resolução .
Comparação de imagens obtidas por um microsca³pio de luz sem o metamaterial
hiperba³lico (coluna da esquerda) e com o metamaterial hiperba³lico (coluna da direita):
duas contas fluorescentes próximas (linha superior), pontos qua¢nticos (linha do meio)
e filamentos de actina nas células Cos-7 (linha inferior).
Crédito: Nature Communications
Os pesquisadores testaram sua tecnologia com um microsca³pio invertido comercial. Eles foram capazes de criar imagens de caracteristicas finas, como filamentos de actina, em células Cos-7 marcadas com fluorescaªncia - caracteristicas que não são claramente discernaveis usando apenas o pra³prio microsca³pio. A tecnologia também permitiu aos pesquisadores distinguir claramente minaºsculos gra¢nulos fluorescentes e pontos qua¢nticos espaa§ados entre 40 e 80 nana´metros.
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A tecnologia de super resolução tem grande potencial para operação em alta velocidade, disseram os pesquisadores. Seu objetivo éincorporar alta velocidade, super resolução e baixa fototoxicidade em um sistema para imagens de células vivas.
Renderização artastica da nova tecnologia de microscopia de super resolução. As células
animais (vermelhas) são montadas em uma lâmina revestida com o metamaterial
hiperba³lico multicamadas. A luz estruturada em nanoescala (azul) égerada pelo
metamaterial e então ilumina as células animais. Crédito: Yeon Ui Lee
A equipe de Liu agora estãoexpandindo a tecnologia para fazer imagens de alta resolução no espaço tridimensional. Este trabalho atual mostra que a tecnologia pode produzir imagens de alta resolução em um plano bidimensional. A equipe de Liu publicou anteriormente um artigo mostrando que essa tecnologia também écapaz de gerar imagens com resolução axial ultra-alta (cerca de 2 nana´metros). Eles agora estãotrabalhando para combinar os dois.